machine vision phd positions

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PhD Positions in Computer Vision

December 2024 or soon after. The candidates must have obtained the MSc degree before starting in the position. The successful candidates will be part of the Milestone Research Program at AAU, and Visual

PhD Studentship: Video Understanding in First-Person Vision (Deep learning, Computer vision , Video representation)

and Economy at the Streatham Campus in Exeter. Project Description: Event recognition in videos has emerged as one of the most dynamic areas in computer vision . Researchers have extensively explored a

2025 RTP round - Computer vision assisted digital twins for civil infrastructure monitoring.

a digital format. Computer visions techniques have been recently developed for structural health monitoring of civil structures, including vibration displacement measurements, crack detection and

2025 RTP round - Transforming Smart Bridge Monitoring by Computer Vision and Edge Computing .

-consuming and costly. In recent years, computer vision techniques have shown promise in bridge displacement measurement and damage detection. Aims The overall aim of this project is to develop a cost

PhD student in Machine Learning and Computer Vision

Published: 2024-06-19 Are you interested in working with neural language models with applications in computer vision , with the support of competent and friendly colleagues in an international

PhD Position in Machine Learning and Computer Vision

Positions: We have an opening for a PhD position in the areas of machine learning and computer vision . The position is supported financially by a SNSF project, whose aim is to build controllable

PhD Studentship: Rolls-Royce sponsored PhD scholarship – Computer vision and robot control for performing on-wing repair of aero-engines

combination of academic and industrial challenges which will enhance the student’s ability to tackle complex intellectual and practical aspects of computer vision and robotics. We are seeking talented

PhD Studentship: Rolls-Royce Sponsored PhD Scholarship - Computer Vision and Robot Control for Performing On-wing Repair of Aero-Engines

of computer vision and robotics. We are seeking talented candidates with: ◾First or upper second class degree in Robotics/ Computer Science/Cybernetics/Mechatronics/Mechanical or related scientific discipline

PhD Position in Photogrammetry and Computer Vision

Photogrammetry and Computer Vision .

PhD student in energy and environmental engineering with a focus on artificial intelligence and computer vision

computer vision , chemometrics and aquaphotomics. These algorithms will be used to develop soft sensors to enable real-time phenotyping and water quality monitoring, with the eventual goal of utilising

Searches related to computer vision

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We have 149 computer vision PhD Projects, Programmes & Scholarships

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computer vision PhD Projects, Programmes & Scholarships

Phd position in machine learning and computer vision, phd research project.

PhD Research Projects are advertised opportunities to examine a pre-defined topic or answer a stated research question. Some projects may also provide scope for you to propose your own ideas and approaches.

Funded PhD Project (Students Worldwide)

This project has funding attached, subject to eligibility criteria. Applications for the project are welcome from all suitably qualified candidates, but its funding may be restricted to a limited set of nationalities. You should check the project and department details for more information.

Neuromorphic Computer Vision: Sensing and Neuromorphic Machine Learning for Vision Applications

Self-funded phd students only.

This project does not have funding attached. You will need to have your own means of paying fees and living costs and / or seek separate funding from student finance, charities or trusts.

Computer Vision with Responsible Artificial Intelligence for Human-Related Data

Human motion analysis using computer vision and artificial inteligence, computing and information sciences doctor of philosophy (ph.d.) degree, computer vision and robot control for performing on-wing repair of aero-engines - (eng - 193), competition funded phd project (students worldwide).

This project is in competition for funding with other projects. Usually the project which receives the best applicant will be successful. Unsuccessful projects may still go ahead as self-funded opportunities. Applications for the project are welcome from all suitably qualified candidates, but potential funding may be restricted to a limited set of nationalities. You should check the project and department details for more information.

Computer vision and machine learning for recognition of civil structural components and their damage analysis

Multimodal understanding of visual and textual information through large language models for advanced cognitive visual tasks, advanced robotics and computer vision: srobo - an intelligent mobile ground robot proficient at autonomously navigating unstructured terrains using image segmentation and object classification, self-supervised learning for complex visual understanding, controlled synthesis of virtual patient populations with multimodal representation learning, development of a next-generation multimodal artificial intelligence platform for early breast cancer diagnosis, funded phd project (uk students only).

This research project has funding attached. It is only available to UK citizens or those who have been resident in the UK for a period of 3 years or more. Some projects, which are funded by charities or by the universities themselves may have more stringent restrictions.

Fully Funded Research Position in ‘Machine Learning for Autonomous Robot Exploration’

Funded phd project (european/uk students only).

This project has funding attached for UK and EU students, though the amount may depend on your nationality. Non-EU students may still be able to apply for the project provided they can find separate funding. You should check the project and department details for more information.

A robust and accurate facial 3D reconstruction method from images acquired by mobile devices at home for facial growth monitoring

Deep learning based machine vision for space-informed applications.

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PhD Position in Machine Learning and Computer Vision

Universitat bern.

Positions: We have an opening for a PhD position in the areas of machine learning and computer vision. The position is supported financially by a SNSF project, whose aim is to build controllable world models with deep learning methods. World models are generative models that predict future outcomes given past ones. We aim to work with both images and audio modalities and be able to use the world models to predict what would happen in the future if a certain condition of interest is met. This project is based on Unsupervised Learning, that is, it uses datasets that do not have human labeling/annotation (eg, text). Research will be performed in the Computer Vision Group at the University of Bern. The position can start from September 1, 2024, at the earliest and will be filled as soon as a suitable candidate is found.

Your profile: We are looking for a highly motivated candidate, who is eager to get involved in cutting edge, creative research. You hold a Master of Science in Computer Science, Mathematics or Engineering, with a solid background in machine learning and computer vision. You have excellent skills in applied mathematics, in probability theory, and a programming language (e.g., Python, C/C++). You have a solid background in Deep Learning and you are already a proficient programmer in one of the main Deep Learning libraries (e.g., TensorFlow, PyTorch). We expect fluent communication skills in English.

What we offer: You will be part of a team of academic researchers working on state of the art technologies for machine learning and computer vision. You will have the chance to contribute to and participate in the international research community. We are located in Bern in the core of Switzerland, one of the cities with the highest quality of life worldwide. You will receive a very competitive salary (a base salary for the research + a separate teaching assistance salary) and be given financial support to attend training courses and international conferences.

Application and further information: Applications must be submitted to Prof Paolo Favaro, through the 'Apply' button. Applications submitted directly via email will not be considered.

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Research Positions in Machine Learning and Computer Vision

Deadline: This submission webpage will be closed as soon as all the positions are filled.

We have an opening for a PhD position in the areas of machine learning and computer vision. The position is supported financially by a SNSF project, whose aim is to build controllable world models with deep learning methods. World models are generative models that predict future outcomes given past ones. We aim to work with both images and audio modalities and be able to use the world models to predict what would happen in the future if a certain condition of interest is met. This project is based on Unsupervised Learning, that is, it uses datasets that do not have human labeling/annotation (eg, text). Research will be performed in the Computer Vision Group at the University of Bern. The position can start from September 1, 2024, at the earliest and will be filled as soon as a suitable candidate is found.

Your profile

We are looking for a highly motivated candidate, who is eager to get involved in cutting edge, creative research. You hold a Master of Science in Computer Science, Mathematics or Engineering, with a solid background in machine learning and computer vision. You have excellent skills in applied mathematics, in probability theory, and a programming language (e.g., Python, C/C++). You have a solid background in Deep Learning and you are already a proficient programmer in one of the main Deep Learning libraries (e.g., TensorFlow, PyTorch). We expect fluent communication skills in English.

What we offer

You will be part of a team of academic researchers working on state of the art technologies for machine learning and computer vision. You will have the chance to contribute to and participate in the international research community. We are located in Bern in the core of Switzerland, one of the cities with the highest quality of life worldwide. You will receive a very competitive salary (a base salary for the research + a separate teaching assistance salary) and be given financial support to attend training courses and international conferences.

Application and further information

• BSc and MSc degree certificates and transcripts;
• 2 reference letters;
• A pdf with the answers to the questionnaire ;
• A Jupyter Notebook with solutions to the coding problems .

You will receive a confirmation by email.

PhD in Machine Learning for 3D Computer Vision

100%, zurich, fixed-term.

The Photogrammetry and Remote Sensing Group at ETH (Prof. Konrad Schindler)  is seeking applications for a doctoral candidate. Our research revolves around computer vision, remote sensing and visual AI.

Job description

We are offering a fully-funded position for a motivated and talented individual to join our research team as a doctoral student and develop generative visual AI models for 3D data. This research opportunity is part of a collaborative project with our partners at Google Research, focusing on developing advanced visual AI models for multiview and 3D-consistent image generation. The successful candidate will have the opportunity to work on pioneering research in AI, contributing to both academic advancements and industry-leading innovations. The position is available from October 2024 onwards.

• MSc degree in computer science, engineering, or a related subject
• Proficiency in programming, preferably in Python and/or Jax
• Strong analytical, mathematical and algorithmic capabilities
• Experience with deep learning technology and computer vision
• Fluent communication in English (both written and spoken)
• Motivation, curiosity and willingness to learn

• An opportunity to further your scientific career at a leading academic institution
• Excellent working conditions
• A chance to work in a stimulating environment as part of a multi-cultural research team
• Exposure to both academia and the commercial tech sector
• High quality of Life in one of the World’s most livable cities

We value diversity

Curious so are we..

If you are interested in joining our team, please submit your complete application online through the ETH job portal :

• motivation letter
• curriculum vitae
• academic transcripts
• contact details of at least two academic referees

The deadline for applications is the 31st of October 2024. Selection will start immediately, so early submissions are encouraged.

Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.

Further information about Photogrammetry and Remote Sensing can be found on our website . Questions regarding the position should be directed to Prof. Konrad Schindler by email [email protected] (no applications).

About ETH Zürich

Help advance the future of computer science

Our teams are innovating at the cutting edge of their fields in order to tackle challenges and build products that impact billions of people every day.

Our mission and philosophy

The research conducted at google has broadened dramatically, becoming more important to our mission than ever before..

We aim to create a research environment rich in opportunities for product impact, to build a product environment that actively benefits from research, and to provide our staff the freedom to work on important research problems that go beyond immediate product needs.

Explore our locations

Offices around the world.

From Accra to Zürich, to our home base in Mountain View and beyond, we’re looking for talented, creative computer scientists to drive our work forward.

North America

Our teams in Atlanta focus on theoretical and application aspects of computer science with a strong focus on machine learning and the algorithmic foundations and theoretical underpinnings of deep learning, with applications to natural language understanding, machine perception, robotics, and ubiquitous computing and sensing.

Our teams in Cambridge work closely with academics at local universities as well as collaborators at local institutes with a goal to impact both Google’s products and general scientific progress. We accomplish this by releasing open source tools, publishing our work and sharing our findings with the academic community.

More boardshorts than boardroom, high tech meets high tide at Google L.A. Our engineers work on such high-impact products as Ads, Chrome, and YouTube, while our sales teams push the limits of digital advertising for top-tier clients. Take advantage of our picture-perfect SoCal weather by hitting the rock wall and elevate team strategy sessions with a game of oversized chess on the roof deck. In-house coffee and juice bars provide pick-me-ups, and beach breaks double as brainstorm sessions when you borrow one of our 4-seat surrey bikes, beach cruisers, or surfboards and head to the boardwalk.

Google Research in Montreal performs both open-ended and applied research, in numerous areas including reinforcement learning, meta-learning, optimization, program synthesis, generative modeling, machine translation, and more. We also support the local academic community and have several academic collaborations, including with Mila – Quebec Artificial Intelligence Institute.

Our headquarters has come a long way from its humble roots in a Menlo Park garage, but our innovative Silicon Valley spirit is stronger than ever. On our largest campus, we work on cutting-edge products that are changing the way billions of people use technology. Onsite benefits like fitness and wellness centers embody our philosophy that taking care of Googlers is good for all of us. Build team skills with a group cooking class or coffee tasting, ride a gBike to one of our cafés, or work up a sweat in a group class. Here at the Googleplex, we’re looking for innovators, collaborators, and blue-sky thinkers. We’re looking for you.

We work in close collaboration with academia, with a goal to impact both Google’s products and general scientific progress. We accomplish this in two ways: by releasing software libraries, a way to build research findings into products and services, and through publishing our work and sharing our findings with the academic community.

Our team in Pittsburgh conducts research in natural language processing, machine learning, image and video understanding, and optimization, and our impacts range from academic paper publications to software systems used throughout Google. We collaborate closely with research and applied groups in many areas, and also work closely with Carnegie Mellon University and other organizations in the extremely strong computer science community in Pittsburgh.

As our company headquarters, Mountain View and the surrounding offices in Sunnyvale, San Francisco, and San Bruno are home to many of our world-class research teams and the innovative projects they work on.

Our research teams in Seattle and Kirkland work on a wide range of disciplines — from quantum computing to applied science to federated learning and health. In doing the above, and more, a large focus of our work also focuses on advancing the state of the art in machine learning.

Nestled between the Santa Cruz Mountains and the San Francisco Bay, with San Jose to the south, San Francisco to the north, and NASA right next door, you’ll find one of Google’s largest and newest global campuses in Sunnyvale. Here in the heart of the original Silicon Valley innovation is happening everywhere—from our Cloud team developing exciting new products and services, to moving into our latest office spaces which include interconnected building projects, the creation of green spaces connecting campuses with the community, and the creative restoration of local habitats. We love growing in Sunnyvale—and you will too.

We develop novel neural network architectures and learning algorithms, with applications to computer vision, natural language and speech processing, medical image analysis, and computer architecture and software.

Europe, Middle East, and Africa

Google Research teams in Accra collaborate with global research teams to lead many sustainability initiatives of particular interest to Africa. We implement theoretical and applied artificial intelligence with a strong focus on machine learning and algorithmic foundations to tackle some global challenges, such as food security, disaster management, remote sensing, among others.

Researchers in our Amsterdam office push the boundaries of what is possible in many domains, including natural language understanding, computer vision and audio, reinforcement learning and machine learning for the natural sciences.

In Berlin, our teams work on a range of topics from foundational to more applied and involve data comprised of text, images, video, audio and more. We are engaging and collaborating closely with Berlin’s vibrant academic and startup communities.

We work on machine learning, natural language understanding and machine perception, from foundational research to AI innovations, in search, healthcare, and crisis response.

We work on natural language understanding and conversational dialog, text-to-speech, (on-device) machine learning, human-centered AI research and user research as well as healthcare.

We work on problems in quantum computing as well as speech and language processing, and collaborate closely with Google’s product teams across the world.

The Nairobi Research Center is dedicated to advancing research in Food Security and Agriculture. Located in the vibrant capital of Kenya, the center brings together a diverse team of experts that address global food challenges through the development of cutting-edge solutions that shape a brighter future for generations to come.

We tackle big challenges across several fields at the intersection of computer science, statistics and applied mathematics while collaborating closely with a strong academic community.

We solve big challenges in computer science, with a focus on machine learning, natural language understanding, machine perception, algorithms and data compression.

Asia-Pacific

Google Research Australia aims to advance the state-of-the-art in machine learning, in areas such as Fundamental Machine Learning, Natural Language Understanding, and Systems Programming. We aim to apply our research in ways that benefit Australia, Google and global society.

We are interested in advancing the state of the art and applications in areas like Machine Learning, Natural Language Understanding, Computer Vision, Software Engineering and Multi-agent Systems.

We are interested in advancing the state of the art and applications in areas like machine learning, speech, and natural language processing.

Meet the teams driving innovation

Our teams advance the state of the art through research, systems engineering, and collaboration across Google.

Our impact reaches billions

Google Research tackles challenges that define the technology of today and tomorrow.

Watch the film

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Find your research career at Google

Our researchers are embedded in teams across computer science, to discover, invent, and build at the largest scale.

Our research-focused software engineers are embedded throughout the company, allowing them to setup large-scale tests and deploy promising ideas quickly and broadly.

Work across data mining, natural language processing, hardware and software performance analysis, improving compilation techniques for mobile platforms, core search, and much more.

Internships take place throughout the year, and we encourage students from a range of disciplines, including CS, Electrical Engineering, Mathematics, and Physics to apply to work with us.

Collaboration is essential for progress

We’re proud to work with academic and research institutions that push the boundaries of AI and computer science.

Measuring and improving the accuracy, safety, speed, and efficiency of AI technologies.

Working to advance fire modeling tools and fire spread prediction algorithms.

Anthropic, Google, Microsoft and OpenAI are launching the Frontier Model Forum, an industry body focused on ensuring safe and responsible development of frontier AI models.

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PhD student in Machine Learning and Computer Vision - Hiring in process/Finished, not possible to apply

This advert is not available!

Uppsala University, Department of Information Technology

Are you interested in working with neural language models with applications in computer vision, with the support of competent and friendly colleagues in an international environment? Are you looking for an employer that invests in sustainable employeeship and offers safe, favorable working conditions? We welcome you to apply for a PhD position at Uppsala University. 

Uppsala University has a long tradition of successful research – among its alumni are 16 Nobel Prize laureates, including, most recently, Svante Pääbo. The University is unique when it comes to combining IT with wider research, from life sciences to the humanities, and this collaboration is currently facilitated by AI4Research and the Centre for Interdisciplinary Mathematics .

The Department of Information Technology holds a leading position in both research and education at all levels. We are currently Uppsala University's third largest department, have around 350 employees, including 120 teachers and 120 PhD students. Approximately 5,000 undergraduate students take one or more courses at the department each year. You can find more information about us on the Department of Information Technology website .

At the Division of Systems and Control , we develop both theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates expertise from machine learning, optimization, control theory, and applied mathematics, spanning diverse application domains such as medicine, energy systems, biomedical systems, neuroscience, and safety and security.

The Division of Systems and Control enjoys a wide network of strong international collaborators all around the world, for example at the University of Cambridge, University of Oxford, Imperial College, University of British Columbia, University of Sydney, University of Newcastle and Aalto University. We strive for all PhD students to get a solid international experience during their PhD.

The position is a part of the Beijer Laboratory for Artificial Intelligence Research , funded by Kjell and Märta Beijer Foundation. The Beijer Laboratory for Artificial Intelligence Research was established in 2023 at Uppsala University with an ambition to grow activities within the subject of AI, focusing on applications in the life sciences and questions related to societal development .

Project description In this project, the selected candidate will join us in conducting fundamental machine learning research and developing principled foundations of vision-language models, with opportunities to validate the methods on challenging real-world problems involving computer vision. There will be a strong focus on deep learning, image analysis and neural language modeling within the project. Some topics of interest within the project scope include, but are not limited to, designing efficient algorithms for integration of visual and textual modalities, aligning visual and text embeddings into a cohesive embedding space and representation learning. We will consider various forms of data, including text, images and videos.

Technical keywords for the position include: deep learning, image analysis, representation learning and language modeling.

Duties A Ph.D. student is expected to devote their time to graduate education mainly. The rest of the duties may involve teaching at the Department, including also some administration, to at most 20%.

Requirements To meet the entry requirements for doctoral studies, you must

• hold a Master’s (second-cycle) degree in computer science, applied mathematics, engineering physics, electrical engineering, machine learning, data science, image analysis, robotics or in a similar field, or
• have completed at least 240 credits in higher education, with at least 60 credits at Master’s level including an independent project worth at least 15 credits, or
• have acquired substantially equivalent knowledge in some other way.

Read about the specific requirements in the study syllabus for the subject . 

We are looking for candidates with

• a strong interest in deep learning, image analysis and neural language modeling,
• good communication skills with sufficient proficiency in oral and written English,
• excellent study results,
• programming proficiency (preferably in Python),
• personal characteristics, such as a high level of creativity, thoroughness, and/or a structured approach to problem-solving are essential.

Additional qualifications Experience and courses in one or more subjects are valued: statistical machine learning, optimisation, linear algebra, deep learning, natural language processing, image analysis and computer vision.

Rules governing PhD students are set out in the Higher Education Ordinance chapter 5, §§ 1-7 and in Uppsala University's rules and guidelines .

Application The application must include:  1) a statement (at most 2 pages) of the applicant’s motivation for applying for this position, including a self-assessment on why you would be the right candidate for this position;  2) a CV; 3) degrees and transcript of records with grades (translated to English or Swedish);  4) the Master’s thesis (or a draft thereof, and/or some other self-produced technical or scientific text), publications, and other relevant documents;  5) references with contact information (names, emails and telephone number) and up to two letters of recommendation.    Applicants who meet at least one of the entry requirements are strongly encouraged to apply. All applicants should state their earliest possible starting date.

About the employment The employment is a temporary position according to the Higher Education Ordinance chapter 5 § 7 . Scope of employment 100 %. Starting date 1 October 2024   or as agreed. Placement: Uppsala. 

For further information about the position, please contact: Assistant Professor Ekta Vats, e-mail: [email protected]. .

Please submit your application by 12 August 2024, UFV-PA 2024/2352.

Are you considering moving to Sweden to work at Uppsala University? Find out more about what it´s like to work and live in Sweden .

Uppsala University is a broad research university with a strong international position. The ultimate goal is to conduct education and research of the highest quality and relevance to make a difference in society. Our most important asset is all of our 7,600 employees and 53,000 students who, with curiosity and commitment, make Uppsala University one of Sweden’s most exciting workplaces.

Read more about our benefits and what it is like to work at Uppsala University https://uu.se/om-uu/jobba-hos-oss/

The position may be subject to security vetting. If security vetting is conducted, the applicant must pass the vetting process to be eligible for employment.

Please do not send offers of recruitment or advertising services.

Submit your application through Uppsala University's recruitment system.

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PhD in Machine Learning for 3D Computer Vision

About the employer.

ETH Zürich is well known for its excellent education, ground-breaking fundamental research and for implementing its results directly into practice.

The Photogrammetry and Remote Sensing Group at ETH (Prof. Konrad Schindler)  is seeking applications for a doctoral candidate. Our research revolves around computer vision, remote sensing and visual AI.

Job description

We are offering a fully-funded position for a motivated and talented individual to join our research team as a doctoral student and develop generative visual AI models for 3D data. This research opportunity is part of a collaborative project with our partners at Google Research, focusing on developing advanced visual AI models for multiview and 3D-consistent image generation. The successful candidate will have the opportunity to work on pioneering research in AI, contributing to both academic advancements and industry-leading innovations. The position is available from October 2024 onwards.

• MSc degree in computer science, engineering, or a related subject
• Proficiency in programming, preferably in Python and/or Jax
• Strong analytical, mathematical and algorithmic capabilities
• Experience with deep learning technology and computer vision
• Fluent communication in English (both written and spoken)
• Motivation, curiosity and willingness to learn
• An opportunity to further your scientific career at a leading academic institution
• Excellent working conditions
• A chance to work in a stimulating environment as part of a multi-cultural research team
• Exposure to both academia and the commercial tech sector
• High quality of Life in one of the World’s most livable cities

We value diversity

Curious so are we..

If you are interested in joining our team, please submit your complete application online through the ETH job portal :

• motivation letter
• curriculum vitae
• academic transcripts
• contact details of at least two academic referees

The deadline for applications is the 31st of October 2024. Selection will start immediately, so early submissions are encouraged.

Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.

Further information about Photogrammetry and Remote Sensing can be found on our website . Questions regarding the position should be directed to Prof. Konrad Schindler by email [email protected] (no applications).

About ETH Zürich

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• Posted on: 11 August 2023

PhD on Explainable AI in Machine Vision for Autonomous Driving

Job information, offer description.

• Are you inspired by the prospect of shaping the future of autonomous driving?
• Are you fascinated by explainable artificial intelligence?
• Are you eager to work on an interdisciplinary team that combines engineering and the humanities?
• Then apply for the PhD position on Explainable AI in Machine Vision for autonomous driving!

Job Description Autonomous driving is a key application of artificial intelligence, and machine vision specifically. Although contemporary machine vision systems now routinely outperform their biological counterparts, they are far from perfect, especially when they are integrated with the complex action-selection systems that drive autonomous vehicles. Methods from explainable AI (XAI) are increasingly used to evaluate and improve the performance of AI systems. Although the technical implementation of these methods is becoming increasingly routine, it remains unclear exactly how these methods can be used most effectively to ensure the safe, responsible, and transparent artificial intelligence. For example, although XAI methods can be used to precisely characterize an AI system's classification performance, it remains unclear how much error can and should be tolerated in this performance. Moreover, although other XAI methods can tell us which factors are actually considered when decisions are being made, it remains unclear which factors are permissible and which ones are not. This PhD project is designed to identify systemize and evaluate XAI methods, and to identify best-practices for machine vision in the context of autonomous driving, while taking into account human factors and societal norms. To this end, it will be necessary to not only consider mathematical and technical details, but also relevant insights from e.g. psychology of human decision-making, regulation & standardization of explainability, and ethical principles of safety, transparency, privacy, and fairness. More specifically, research tasks will include:

• Reviewing relevant literature from the social sciences and humanities on AI safety and explainability.
• Reviewing technical literature on machine learning and explainable AI.
• Developing a normative evaluation framework for the use of explainable AI in machine vision for autonomous driving.
• Collaborating on ongoing engineering projects that aim to implement XAI methods in machine vision for autonomous driving.

As this is an inherently interdisciplinary research project, the ideal candidate will combine technical expertise in machine learning and explainable AI (e.g. visualization techniques and feature-importance measures) with an ability to engage relevant issues in social science and humanities (in particular, norms of explainability and AI safety). The candidate will be integrated in the Mobile Perception Systems (MPS) lab as well as the Philosophy & Ethics (P&E) group. They will be a member of the LTP ROBUST consortium funded by NWO and NXP Semiconductors, and of the EAISI institute at TU/e.

Where to apply

Requirements.

• A master's degree (or an equivalent university degree) in computer science, cognitive science, or a related discipline.
• Demonstrable interest and experience in relevant social science and humanities fields such as philosophy, psychology, and/or governance.
• Demonstratable engineering and programming skills required for AI research (e.g. experience with deep learning frameworks like PyTorch or Tensorflow).
• A research-oriented attitude.
• Ability to work in an interdisciplinary team and interested in collaborating with industrial partners.
• Motivated to develop teaching skills and coach students.
• Fluent in spoken and written English (C1 level).

Additional Information

A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:

• Full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months. You will spend 10% of your employment on teaching tasks.
• Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. €2,770 max. €3,539).
• A year-end bonus of 8.3% and annual vacation pay of 8%.
• High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process .
• An excellent technical infrastructure, on-campus children's day care and sports facilities.
• An allowance for commuting, working from home and internet costs.
• A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates.

About us Eindhoven University of Technology is an internationally top-ranking university in the Netherlands that combines scientific curiosity with a hands-on attitude. Our spirit of collaboration translates into an open culture and a top-five position in collaborating with advanced industries. Fundamental knowledge enables us to design solutions for the highly complex problems of today and tomorrow. Curious to hear more about what it's like as a PhD candidate at TU/e? Please view the video. Do you recognize yourself in this profile and would you like to know more? Please contact the hiring managers Dr. Carlos Zednik, c.a.zednik[at]tue.nl and Dr. Hala Elrofai, h.b.h.elrofai[at]tue.nl. Visit our website for more information about the application process or the conditions of employment. You can also contact Hanneke Huijs-Palmen, HR Advisor, j.b.huijs.palmen[at]ue.nl or +31 40 2472137. Are you inspired and would like to know more about working at TU/e? Please visit our career page . Application We invite you to submit a complete application by using the apply button. The application should include a:

• Cover letter in which you describe your motivation and qualifications for the position.
• Curriculum vitae, including a list of your publications and the contact information of three references.
• A writing sample (ca. 10 pages) which demonstrates your ability to combine technical knowledge and expertise with analytic techniques from the social sciences and humanities.

We look forward to receiving your application and will screen it as soon as possible. The vacancy will remain open until the position is filled.

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PhD Position in Computer Vision and Machine Learning

NOTE: this position listing has expired and may no longer be relevant!

Position Description

Autonomous Vehicles at the Intersection of Computer Vision, Machine Learning and Human Factors

About the project

Institute for Transport Studies (ITS) is the leading transportation research centre in the UK and one of the Top 10 global leaders in the field. The centre currently has 60+ research staff and PhD students in multi-disciplinary areas of Human Factors, Computer Vision, AI and Machine Learning, Behavior Modeling, Psychology, and Transportation Safety.

Highly automated vehicles (AVs) also known as Level 4 vehicles can operate in self-driving mode in most circumstances and the driver is allowed to involve in any sort of non-driving activities, even sleeping. However, the automated driving mode is only expected to perform in limited spatial areas (geofenced) and under specific legislations; therefore, a human driver still has the option of getting ready to resume the driving control, whenever required.

This project aims at promoting public trust in automated driving based on scientific investigations and findings on the role of Computer Vision Technology and Human Factors in a smooth technology adaptation and transition. Using machine learning, computer vision, and human factors, the research should investigate whether the driver is ready to safely and swiftly take over the control of the vehicle? What are the major contributing human factors in taking over scenarios and to what extent? Under what circumstances the sensors and autonomous mode can perform safer than a human driver and vice-versa?

The research may involve in-cabin situation awareness such as driver behaviour monitoring (eye, head pose, and body pose modelling), as well as road hazard perception and traffic condition understanding (vehicles, pedestrians and cyclists’ detection & tracking).

While in this research we put more weight in Computer Vision part of the research, the PhD candidate is encouraged to perform joint research within our great interdisciplinary team of psychologists and human factor experts in ITS.

The PhD student is expected to contribute towards one of the following research pathways with a clear and feasible research plan and proposal for a 3.5-year PhD journey.

Possible Research Plans/Directions: • Development of appropriate methodologies, models, tools, and frameworks to increase automation levels of driving and driver comfort using real-world visual datasets and human factors.

• Using Leeds University Driving Simulator facilities/datasets for eye tracking, driver awareness monitoring, situation awareness monitoring and driver-pedestrian interactions.

• Decision making and risk assessment for switching between human and the automated driving mode, based on real-world visual datasets and sensory information in different driving scenarios.

• Driver behaviour monitoring using visual and psychological measures based on eye and head pose tracking, body pose estimation, and activity recognition.

• In-depth understanding of sensor technologies including Vision, RGB-D, Lidar, Radar, and multi-sensor data fusion research for traffic monitoring and hazard perception.

Entry requirements: – A Masters Degree in a relevant subject e.g. Computer Science, Engineering, AI, Machine Learning. – Solid background in applied Computer Vision and Machine Learning – Demonstrated competency in Math and Statistics – Demonstrated Programming proficiency (Python and/or C) – Minimum IELTS Score of 6.5 (no band less than 6.0)

Desired competencies: – Ability to teamwork in a multidisciplinary research group – Willingness and plans for joint research in human factors, safety, public trust, and social impact of the research. – Prior publications in the field

Country Eligibility: UK/EU/International

How to Apply

Further information about the population structure:

Located in:

• Leningrad Oblast
• Vsevoložskij Rajon

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Leningrad Oblast, Russia

The capital city of Leningrad oblast: Saint Petersburg .

Leningrad Oblast - Overview

Leningrad Oblast is a federal subject of Russia located in the northwest of the European part of the country, part of the North-West Federal District. The regional authorities are located in St. Petersburg (from 1924 to 1991 - Leningrad, hence the name of the region).

The population of Leningrad Oblast is about 1,911,600 (2022), the area - 83,908 sq. km.

Leningrad oblast flag

Leningrad oblast coat of arms.

Leningrad oblast map, Russia

Leningrad oblast latest news and posts from our blog:.

9 September, 2021 / Entertainment near St. Petersburg .

3 June, 2021 / Vyborg Castle - a unique architectural monument for Russia .

2 February, 2021 / Ivangorod Fortress - the western outpost of medieval Russia .

17 October, 2017 / Picturesque views of Vyborg .

22 March, 2017 / Vyborg - the only historic town in the Leningrad region .

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History of Leningrad Oblast

The first settlements on the territory of the present Leningrad region appeared in the 9th-8th millennium BC, after the retreat of the glacier. By the middle of the 1st millennium AD, the Finno-Ugric tribes engaged in agriculture, cattle breeding, hunting, and fishing lived here. In the 8th century, the Slavic tribes came to the region.

In the 750s, Ladoga appeared (since the 18th century - Staraya (Old) Ladoga) - the most ancient Russian settlement. In the 9th-10th centuries, Ladoga became an important political and economic center of the ancient Russian state. At the end of the 10th century, Lagoda lost this role. Novgorod became the new center.

In the 12th century, Novgorod gained political independence, and the lands on the shores of the Gulf of Finland, Luga, Neva, Ladoga, Volkhov became part of the Novgorod Republic. In the 13th-14th centuries, this region became an arena of struggle of Novgorod and Pskov Republics against the Livonian knights and Swedish feudal lords.

More historical facts…

In 1240, the famous Battle of the Neva took place in which the Novgorod troops headed by Prince Alexander Yaroslavich defeated the Swedish troops. After the battle, Alexander Yaroslavich received an honorary nickname of “Nevsky” and became known simply as Alexander Nevsky.

In the 13th-14th centuries, for the protection of the north-western borders, several new fortresses were founded: Yam, Koporje, Oreshek, Korela, Tiversky town. In January, 1478, the Novgorod Republic was conquered by the Moscow principality. The region became part of the united Russian state.

In the early 17th century, during the Time of Troubles, Russia was cut off from the Baltic Sea. The north-east of the country was captured by the Swedes. In the early 18th century, during the Northern War, Russia regained control of the region and Saint Petersburg, a new capital of the state, was founded here.

In 1708, the Ingermanland region was formed. In 1710, it was renamed into Saint Petersburg, in 1914 - Petrograd, in 1924 - Leningrad oblast. During the Second World War, a large part of the territory was occupied by the Germans and the Finns and suffered greatly. In January 1945, the population of the region was 483,000. The population before the war was 1,258,000.

In the postwar period, the regional economy was restored, a number of new towns and villages appeared. In 1973, the Leningrad nuclear power plant was opened in Sosnovy Bor.

Nature of Leningrad Oblast

Leningrad Oblast landscape

Author: Roman Rozanov

Leningrad Oblast nature

Author: Igor Los

Field in the Leningrad region

Author: Vitaliy Karputkin

Leningrad Oblast - Features

St. Petersburg is not part of the Leningrad region. It is an independent subject of the Russian Federation, a city of federal significance. Leningrad Oblast is the only federal subject of Russia the authorities of which are located entirely on the territory of another federal subject.

Leningrad Oblast, located on the East European (Russian) Plain, stretches from west to east for 500 km, from north to south - 320 km. The region borders with the European Union: in the west - with Estonia, in the north-west - with Finland.

The climate is continental. Marine air masses cause relatively mild winter with frequent thaws and moderately warm, sometimes cool summer. The average temperature in January is in the range of minus 8-11 degrees Celsius, in July - plus 16-18 degrees Celsius.

Leningrad Oblast is rich in water resources - rivers and lakes. There are about 1,800 lakes including Ladoga Lake - the largest lake in Europe. Much of the region is occupied by marshes.

Industry is the backbone of the local economy. Such industries as food industry, transport equipment, oil product, pulp and paper industry prevail. Part of the Nord Stream gas pipeline passes through the territory of the region.

Leningrad Nuclear Power Plant is situated in the town of Sosnovy Bor. Machine-building plants are situated in different towns of the province. Ford Automobile Plant is situated in Vsevolozhsk, Caterpillar - in Tosno.

The transport network is well developed in the region. Saint Petersburg is one of the largest transport hubs of Russia.

The largest towns of Leningrad Oblast are Murino (89,600), Gatchina (87,600), Vsevolozhsk (76,600), Vyborg (73,000), Sosnovy Bor (66,000), Sertolovo (61,200), Tikhvin (56,700), Kudrovo (54,500), Kirishi (49,600), Kingisepp (43,900) Volkhov (43,300).

Attractions of Leningrad Oblast

In 1990, a number of monuments of history and culture of Leningrad Oblast were included into the list of UNESCO World Heritage:

• The fortifications on the shore of the Gulf of Finland: “Seraya Loshad” (Grey Horse) and “Krasnaya Gorka” (Red Hill),
• The historical part of the town of Shlisselburg,
• The fortress “Oreshek”,
• The palace and park ensembles in Ropsha, Gostilitsy, Taytsy,
• The palace and park ensemble and the historical center of Gatchina,
• Sologubovka War Memorial - the largest German military cemetery.

Other places of interest:

• Staraya (Old) Ladoga - the ancient capital of northern Russia,
• Vyborg Castle,
• Assumption Monastery, the house-memorial of Rimsky-Korsakov in Tikhvin,
• Ivangorod Fortress,
• “Korela” fortress in Priozyorsk,
• Koporye fortress in Lomonosov district,
• “Priyutino” museum estate in Vsevolozhsk,
• Konevsky Monastery on Konevets Island,
• Wooden churches of Svir (“Golden Ring of Leningrad oblast”) in Podporozhsky district,
• Tervenichesky Convent,
• Zelenetskii Trinity Monastery,
• Lisino - the buildings designed by N.L.Benua,
• Museum estate of N.K. Roerich in Izvara,
• Museum “Stationmaster House” in Vyra.

Leningrad oblast of Russia photos

Leningrad Oblast scenery

Abandoned wooden church in Leningrad Oblast

Author: Sergei Baranov

Village of Leningrad Oblast

Author: Mikhail Petrov

Pictures of Leningrad Oblast

Classical Russian dacha in the Leningrad region

Author: Moskaluk Vitaliy

Wooden village house in Leningrad Oblast

Author: Zhelne Yuri

Winter in Leningrad Oblast

Author: Stanislav Kostin

Leningrad Oblast views

Railway bridge in Leningrad Oblast

Author: Antonio Racter

Abandoned church in the Leningrad Oblast

Author: Logutenkov Andrey

Church in Leningrad Oblast

Author: Semyonov A.V.

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