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Lung cancer remains the leading cause of cancer-related deaths worldwide. AI has recently emerged as a transformative tool for enhancing medical decision-making. However, its widespread adoption faces several challenges, including data quality, model transparency, and interpretability. This thesis seeks to explore how innovative AI techniques can revolutionize lung cancer research and treatment, offering new opportunities to address these challenges. It aims to contribute to the broader application of AI in healthcare.

This project offers the candidate a unique opportunity to apply artificial intelligence techniques to real-world challenges in lung cancer research and treatment. As part of this thesis, the candidate will work with datasets, including patient records, genetic data, molecular alterations, treatment outcomes, and exposome data. These datasets will serve as the foundation for developing AI models that address critical challenges in lung cancer treatment, such as predicting patient outcomes and identifying optimal treatment strategies.

The candidate will focus on the following core tasks:

Data Exploration and Preprocessing: The candidate will gain experience in handling complex medical datasets by cleaning, preparing, and structuring the data to ensure it is suitable for advanced AI analysis.

Building AI Models: Using machine learning and deep learning techniques, the candidate will develop models aimed at predicting lung cancer progression, evaluating treatment efficacy, and understanding the impact of various environmental and genetic factors.

Interpretability and Explainability: A significant emphasis will be placed on making AI models interpretable and transparent. The candidate will explore techniques to ensure that the models produced are not just accurate but also explainable, providing healthcare professionals with clear insights into the model's predictions and decisions.

Exploring Interaction Networks: The candidate will analyze interaction networks, studying relationships between patient genetics, environmental factors, and treatment responses to identify key drivers of lung cancer outcomes.

Throughout the project, the candidate will not only gain hands-on experience with cutting-edge AI tools and methodologies but also develop a deeper understanding of AI's role in healthcare. This project provides an impactful opportunity to contribute to a field where AI innovation can directly improve patient outcomes.

Depression and anxiety are among the most significant health issues worldwide, affecting up to 50% of the population during their lifetime (Santomauro et al., 2021). The aim of this project is to train automated algorithms to identify "cognitive distortions" from clinical data in the Spanish

Proposal 1: Automated Detection of Cognitive Distortions: Enhancing Mental Health Diagnosis in Spanish-Speaking Populations

Depression and anxiety are among the most significant health issues worldwide, affecting up to 50% of the population during their lifetime (Santomauro et al. , 2021). Despite the effectiveness of psychological interventions in reducing their impact, these conditions are often underdiagnosed and undertreated, primarily due to limited human resources. When individuals are depressed or anxious, their information processing tends to be biased, leading to "cognitive distortions" For example, a common thought among depressed individuals is: "I am a failure." The development of Natural Language Processing (NLP) offers a promising opportunity to improve the detection of these cognitive distortions automatically. Previous studies have aimed at identifying cognitive distortions using posts from online mental health platforms, annotated by mental health professionals (Simms et al. , 2017; Rojas-Barahona et al. , 2018; Shreevastava and Foltz, 2021) and people lacking clinical experience (Shickel et al. , 2019). However, these datasets do not represent individuals with depressive or anxiety disorders in clinical settings. Furthermore, in the Spanish language, there is a significant lack of data available for such analysis.

To train automated algorithms to identify "cognitive distortions" from clinical data in the Spanish language.

• Generation of the Dataset: Expert clinical psychologists from the Hospital Clínic de Barcelona have generated 1500 quotes containing ten types of "cognitive distortions" inspired by patients with depression and anxiety ( Table 1 ). As control quotes, the psychologists have also produced non-distorted versions of the same thoughts, known as "alternative thoughts" (Beck, 2005).
• Annotation of the Dataset: Clinical psychologists, who were blind to the generated dataset, were presented with random quotes and instructed to determine whether each quote contained cognitive distortions using binary distorted/non-distorted labels. If a quote was considered "distorted," they were then asked to identify the type of cognitive distortion present from among the ten predefined types. Given that cognitive distortions can overlap (i.e., a single quote can exhibit multiple types of distortions), annotators were allowed to select multiple types of distortion. To ensure a high-quality dataset, a representative sub-corpus (10%) of the dataset was first annotated. Inter-annotator agreement metrics were calculated, and the annotation system was updated based on suggestions from the annotators. Once the initial 10% was satisfactorily annotated, the remaining 90% of the dataset was annotated. Disagreements during this phase were debated among the clinical psychologists, and a consensus was reached whenever possible to finalize the annotations.
• Detect Cognitive distortions: Develop algorithms to distinguish between cognitive distortions and non-distorted thinking. High specificity is crucial to avoid invalidating patients' emotions, which could be counterproductive for psychotherapeutic intervention.
• Detect the Type of Cognitive distortions: The first step in any psychotherapeutic intervention is psychoeducation, which involves informing patients about their symptoms and diagnoses. For cognitive therapy, this includes identifying and explaining the types of cognitive distortions. Therefore, it is important to accurately identify both the presence and type of cognitive distortions.

Various NLP and machine learning methods will be tested for both purposes. Classification accuracies will be compared, and the best-performing algorithm will be selected for each task. The specific language cues used for classification will be studied to understand and make the classification systems transparent for clinical applicability.

Our previous work on the same dataset, involving uncovering structural and emotional patterns in cognitive distortions using NLP and cognitive network science (Molins et al., in preparation), revealed several key findings. We found that cognitive distortions, compared to alternative thoughts, contained more words, exhibited increased negative valence and excitatory arousal, showed higher structural imbalance and less compartmentalization, and were characterized by higher incoherence. Cognitive distortions were also associated with more negative emotions, such as anger, disgust, and sadness. Additionally, different types of cognitive distortions displayed distinct characteristics: for example, catastrophism was associated with higher structural imbalance, different central actors, and increased sadness, while labeling was linked to higher levels of disgust. This nuanced understanding can be leveraged to train more effective detection algorithms.

Developing automated models for detecting cognitive distortions will likely enhance the identification of individuals experiencing depression and anxiety. These models can be applied to social media platforms (Ramírez-Cifuentes et al. , 2020) and mental health chatbots (Anmella et al. , 2023), which are increasingly used worldwide. By leveraging these technologies, psychotherapeutic interventions can be provided to a broader population, addressing the current shortage of psychologists and mental health professionals. This development is particularly crucial for the Spanish-speaking community, where such models are currently lacking.

Anmella, G. et al. (2023) 'Vickybot, a Chatbot for Anxiety-Depressive Symptoms and Work-Related Burnout in Primary Care and Health Care Professionals: Development, Feasibility, and Potential Effectiveness Studies', J Med Internet Res 2023;25:e43293 https://www.jmir.org/2023/1/e43293 , 25(1), p. e43293. Available at: https://doi.org/10.2196/43293.

Beck, A.T. (2005) 'The current state of cognitive therapy: a 40-year retrospective', Archives of general psychiatry , 62(9), pp. 953¿959. Available at: https://doi.org/10.1001/ARCHPSYC.62.9.953.

Ramírez-Cifuentes, D. et al. (2020) 'Detection of Suicidal Ideation on Social Media: Multimodal, Relational, and Behavioral Analysis', J Med Internet Res 2020;22(7):e17758 https://www.jmir.org/2020/7/e17758 , 22(7), p. e17758. Available at: https://doi.org/10.2196/17758.

Rojas-Barahona, L. et al. (2018) 'Deep learning for language understanding of mental health concepts derived from Cognitive Behavioural Therapy', pp. 44¿54. Available at: https://doi.org/10.18653/v1/w18-5606.

Santomauro, D.F. et al. (2021) 'Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic', Lancet (London, England) , 398(10312), pp. 1700¿1712. Available at: https://doi.org/10.1016/S0140-6736(21)02143-7/ATTACHMENT/927FDFEF-CCD4-4655-AACF-4E7D54DFECF5/MMC1.PDF.

Shickel, B. et al. (2019) 'Automatic Detection and Classification of Cognitive Distortions in Mental Health Text', Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020 , pp. 275¿280. Available at: https://doi.org/10.1109/BIBE50027.2020.00052.

Shreevastava, S. and Foltz, P.W. (2021) 'Detecting Cognitive Distortions from Patient-Therapist Interactions', Computational Linguistics and Clinical Psychology: Improving Access, CLPsych 2021 - Proceedings of the 7th Workshop, in conjunction with NAACL 2021 , pp. 151¿158. Available at: https://doi.org/10.18653/V1/2021.CLPSYCH-1.17.

Simms, T. et al. (2017) 'Detecting Cognitive Distortions Through Machine Learning Text Analytics', Proceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017 , pp. 508¿512. Available at: https://doi.org/10.1109/ICHI.2017.39.

In this thesis, the focus is on understanding emergence in Large Language Models (LLMs). Emergence refers to complex behaviors that arise from interactions among individual components, even when those components lack those behaviors individually. LLMs exhibit surprising linguistic abilities beyond their constituent words or tokens. Assembly Theory (AT) provides a framework for quantifying complexity without altering fundamental physical laws. By applying AT to LLMs, this research aims to uncover how emergent properties emerge from the interplay of simple components.

What is Emergence? Emergence refers to the phenomenon where a complex system exhibits properties or behaviors that its individual components do not possess in isolation. These emergent features arise only when the components interact within a broader context. In philosophy, science, and art, emergence plays a pivotal role in theories related to integrative levels and complex systems. For example, life, as studied in biology, emerges from the underlying chemistry and physics of biological processes.

Emergence in Large Language Models Recent research has highlighted emergent behavior in Large Language Models (LLMs). These models, such as GPT-3, exhibit surprising capabilities beyond their individual components (words or tokens). The interactions between countless parameters give rise to emergent linguistic abilities, including natural language understanding, generation, and context-based reasoning. For further details, refer to https://arxiv.org/pdf/2206.07682

What is Assembly Theory (AT)? Assembly Theory (AT) provides a novel framework for quantifying complexity without altering fundamental physical laws. Unlike traditional point-particle models, AT defines objects based on their potential formation histories. These "objects" can exhibit evidence of selection within well-defined boundaries. AT allows us to explore emergent properties by considering how components assemble into coherent entities, shedding light on the intricate dynamics of complex systems. See this paper for further information: https://www.nature.com/articles/s41586-023-06600-9

Research Goals: This thesis aims to apply Assembly Theory to understand emergence in Large Language Models. We will test AT in this particular set emergence problem.

Bipolar Disorder (BD) is a psychiatric condition in which people experience significant shifts in mood, energy, and thought processes during manic and depressive episodes (Nierenberg et al., 2023). The aim of this project is to correlate speech features with bipolar disorder and to train predictive models for diagnosis.

Proposal 2: Automated Speech Analysis in Bipolar Disorder: Enhancing Diagnosis and Monitoring.

Bipolar Disorder (BD) is a psychiatric condition in which people experience significant shifts in mood, energy, and thought processes during manic and depressive episodes (Nierenberg et al. , 2023). Manic episodes involve heightened energy, rapid speech, and grandiose thoughts, while depressive episodes are marked by low energy, slow speech, and feelings of hopelessness.

Language, expressed through speech, provides a privileged window into the mind and is thus a cornerstone of psychiatric evaluation. During these evaluations, clinicians routinely assess speech features such as (A) acoustic properties, (B) formal aspects, (C) language content, and (D) emotionality, albeit subjectively.

• Acoustic features ( Table 1 ) (e.g., pitch and volume) reflect mood states (Low, Bentley and Ghosh, 2020). Manic episodes might be characterized by increased pitch and loudness, while depressive episodes may present with reduced pitch variation and monotonous speech.
• Formal aspects ( Table 2 ) (e.g., flow, fluency, rhythm, quantity, and latency) can delineate distinct patterns associated with different phases (euthymia, mania, depression) of BD (Weiner et al. , 2019). Overly rapid speech may indicate manic thought patterns, whereas slowed, halting speech can suggest depressive cognition.
• Language content features ( Table 3 ) (e.g., coherence and cognition) reflects thought organization (Iter, Yoon and Jurafsky, 2018). Manic episodes may involve tangential or circumstantial speech, while depressive episodes often include pervasive negative thoughts.
• Emotionality features ( Table 4 ) (e.g., emotional valence and content) helps differentiate manic and depressive phases (Khorram et al. , 2018). Manic speech often conveys exaggerated positive emotions, whereas depressive speech typically reflects sadness and despair.

Modern technology enables high-fidelity speech recording and automated analysis of these features (DeSouza et al. , 2021), showing potential in diagnosing and monitoring BD (Guidi et al. , 2015; Faurholt-Jepsen et al. , 2016).

We hypothesized that (i) speech features will correlate with the severity of manic and depressive symptoms, (ii) they will effectively differentiate between manic, depressive, and euthymic phases in BD, as well as between mania/depression and response/remission, (iii) only specific speech features and speech tasks will be relevant for each of these analyses.

• To correlate speech features with manic-depressive symptoms severity in BDf
• To use speech features to develop predictive models for diagnostic (i.e., manic, depressive, and euthymic phases in BD), and treatment outcomes (i.e., acute phases of mania/depression vs response/remission)
• To determine which specific speech features and speech tasks are most relevant for each of the previous analyses.

A naturalistic, observational study was conducted. Patients with BD experiencing manic and depressive episodes underwent longitudinal audio recording during acute phases and after response/remission using a dual-microphone setup. Patients during euthymia (mood stability) were recorded once. Interviews included clinical evaluation, cognitive tasks, standard text reading, and emotional and non-emotional storytelling ( Figure 1 ).

Audio recordings from 76 patients (24 manic, 21 depressed, 31 euthymic) were collected. Among the patients in the acute phase, 17 manic patients and 9 depressed patients were recorded longitudinally at clinical remission. Sociodemographic and clinical data of the sample are reported in Table 5 . The average age was 48.1±13.3 years, and 64.6% were female. The mean Young Mania Rating Scale (YMRS) score for manic patients was 24±8.5 (moderate-to-severe) during acute episodes, reducing to 5.9±6.2 (mild-to-minimal symptoms) after response/remission. Depressed patients had a mean Hamilton Depression Rating Scale (HDRS-17) score of 17.1±4.4 (moderate-to-severe) during acute episodes, decreasing to 3.3±2.8 (mild-to-minimal symptoms) after remission. Euthymic patients exhibited mild-to-minimal symptoms (mean YMRS score of 0.97±1.4 and an HDRS-17 score of 3.9±2.9).

Recordings were automatically diarized and transcribed.

• To extract speech features from the audio recordings, including (A) acoustic properties, (B) language content, (C) formal aspects, and (D) emotionality.
• Statistical analyses:
• To correlate speech features with clinical scales, 
• To use lasso logistic regression to develop predictive models for diagnostic and treatment outcomes using speech features, and 
• To use variable relevance methods to identify the most pertinent speech features for these analyses, and assessing the magnitude of correlation and prediction accuracies across different speech tasks.

Impact : Automated speech analysis in BD might provide objective, quantitative markers for psychopathological (manic/depressive) alterations. This technology could potentially identify subtle alterations imperceptible to clinicians, signaling early signs of acute relapse and allowing for early intervention. Implementing this technology could improve diagnosis, monitoring, and prediction of treatment response.

DeSouza, D.D. et al. (2021) 'Natural Language Processing as an Emerging Tool to Detect Late-Life Depression', Frontiers in Psychiatry . Frontiers Media S.A. Available at: https://doi.org/10.3389/fpsyt.2021.719125.

Faurholt-Jepsen, M. et al. (2016) 'Voice analysis as an objective state marker in bipolar disorder', Translational psychiatry , 6, p. e856. Available at: https://doi.org/10.1038/tp.2016.123.

Guidi, A. et al. (2015) 'Voice quality in patients suffering from bipolar disease', in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS . Institute of Electrical and Electronics Engineers Inc., pp. 6106¿6109. Available at: https://doi.org/10.1109/EMBC.2015.7319785.

Iter, D., Yoon, J. and Jurafsky, D. (2018) 'Automatic Detection of Incoherent Speech for Diagnosing Schizophrenia', in. Association for Computational Linguistics (ACL), pp. 136¿146. Available at: https://doi.org/10.18653/v1/w18-0615.

Khorram, S. et al. (2018) 'The PRIORI Emotion Dataset: Linking Mood to Emotion Detected In-the-Wild'. Available at: http://arxiv.org/abs/1806.10658.

Low, D.M., Bentley, K.H. and Ghosh, S.S. (2020) 'Automated assessment of psychiatric disorders using speech: A systematic review', Laryngoscope Investigative Otolaryngology , 5(1), pp. 96¿116. Available at: https://doi.org/10.1002/lio2.354.

Nierenberg, A.A. et al. (2023) 'Diagnosis and Treatment of Bipolar Disorder: A Review', JAMA . American Medical Association, pp. 1370¿1380. Available at: https://doi.org/10.1001/jama.2023.18588.

Weiner, L. et al. (2019) 'Thought and language disturbance in bipolar disorder quantified via process-oriented verbal fluency measures', Scientific Reports , 9(1). Available at: https://doi.org/10.1038/s41598-019-50818-5.

This thesis aims to develop RE-Miner 2.0, an enhanced version of the existing tool. The project will extend RE-Miner's capabilities by integrating additional metrics for analyzing user reviews, focusing on categorizing review types, identifying discussed topics, and assessing the emotional tone and rating patterns within reviews. These additions will allow for a more comprehensive analysis of user feedback, enabling stakeholders to derive actionable insights across diverse review dimensions.

The project will involve redesigning RE-Miner's architecture to support the integration of new analysis pipelines while maintaining modularity and scalability. Advanced natural language processing (NLP) techniques will be employed for tasks such as classification, sentiment analysis, and topic modeling, coupled with interactive visualizations to present the results in an intuitive and accessible manner.

Objectives:

• Extend RE-Miner's architecture to support a broader range of analytical metrics.
• Develop and integrate advanced NLP pipelines for deeper review analysis.
• Enhance data visualization capabilities to present results in a user-friendly and actionable format.

Expected Outcome: An upgraded tool that provides a holistic platform for app review analysis, empowering developers, product managers, and stakeholders to derive insights critical to requirements engineering and product development.

This project will address the problem of optimization of power flows in electric power systems with high penetration of renewable generation using Deep Reinforcement Learning and Graph Neural Networks.

The optimization will ensure the cost and losses minimization while ensuring a safe integration of renewable energy (of variable nature) also considering the availability of flexible demand and energy storage systems. The use cases will include normal operation and also contingencies. The optimization will include security constrained optimal power flow. Some use cases will include large scale continental grids and potentially offshore wind power plants connected to offshore energy hubs. Other use cases will be focused on distribution networks.

A large-scale floating vehicle dataset of per-street segment traffic information, Metropolitan Segment Traffic Speeds from Massive Floating Car Data in 10 Cities (MeTS-10) is available for 10 global cities with a 15-minute resolution for collection periods ranging between 108 and 361 days in 2019¿2021 and covering more than 1500 square kilometers per metropolitan area. Data has been published by HERE. A comparison of the differences across some of the datasets in spatio-temporal coverage and variations in the reported traffic will be addressed in this master thesis.

Traffic analysis is crucial for urban operations and planning, while the availability of dense urban traffic data beyond loop detectors is still scarce. A large-scale floating vehicle dataset of per-street segment traffic information, Metropolitan Segment Traffic Speeds from Massive Floating Car Data in 10 Cities ( MeTS-10 ) is available for 10 global cities with a 15-minute resolution for collection periods ranging between 108 and 361 days in 2019¿2021 and covering more than 1500 square kilometers per metropolitan area. MeTS-10 enables novel, city-wide analysis of mobility and traffic patterns for ten major world cities, overcoming current limitations of spatially sparse vehicle detector data.  MeTS-10 features traffic speed information at all street levels from main arterials to local streets for Antwerp, Bangkok, Barcelona, Berlin, Chicago, Istanbul, London, Madrid, Melbourne , and Moscow . The dataset leverages the industrial-scale floating vehicle Traffic4cast data with speeds and vehicle counts provided in a privacy-preserving spatio-temporal aggregation. Data has to be map-matched to the OpenStreetMap (OSM) road graph. City datasets can be compared with other publicly available stationary vehicle detector data (for Berlin, London, and Madrid) and the Uber traffic speed dataset (for Barcelona, Berlin, and London). A comparison of the differences across some of the datasets in spatio-temporal coverage and variations in the reported traffic will be addressed in this project. The large spatial and temporal coverage offers an opportunity for joining the MeTS-10 with other datasets, such as traffic surveys in traffic planning studies or vehicle detector data in traffic control settings. A pipeline is already available to derive the dataset from the Traffic4cast data published by HERE. HERE is a technologal company providing a platform for the visualization and analysis of location data. To ensure data privacy, the Traffic4cast dataset was published as rasterized and aggregated cell-based data, nevertheless providing a high spatial and temporal resolution.

We want to demonstrate experimentally that augmenting a model with fNIRS data carries neural activity features complementing the information captured by the model and demonstrate that it improves the models' performance. To this end, we will have to collect data from participants and test how different Transformer models benefit from different types of fNIRS attention masks.

Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging technique that measures changes in oxygenated (HbO2) and deoxygenated hemoglobin (HbR) in the cerebral cortex. Due to its portability and low cost, fNIRS has been used in Brain-Computer Interface (BCI) applications, characterizing hemodynamic responses to varying stimuli, and investigating auditory and visual-spatial attention during Complex Scene Analysis (CSA). In this project, we want to design and implement an fNIRS study with a goal of studying the impact of neural and BCI outcomes to improving the training of LAI models' attention mechanism (e.g., Transformer attention) during reading comprehension tasks (e.g., the participants will be judging the quality of generated text). We want to demonstrate experimentally that augmenting a model with fNIRS data carries neural activity features complementing the information captured by the model and demonstrate that it improves the models' performance. To this end, we will have to collect data from participants and test how different Transformer models benefit from different types of fNIRS attention masks.

The candidate will:

• Carry out controlled studies and collect data from a number of participants.
• Try different approaches to incorporating fNIRS signal in the training process of DL/LLM models and compare against baselines/experiments (these should be replicated).
• Carry out ablation studies, personalisation techniques, error analyses, etc.
• Contribute to authoring a scientific article

Internship to develop the TFM on GNN and LLM applied to detection and mitigation of network attacks and anomalies in an AI-based cybersecurity startup.

The internship will be formalized as a CCE (Conveni de Cooperació Educativa).

To apply you need to send an email to pere.barlet@upc.edu with your CV and your bachelor and master transcripts.

We want to demonstrate experimentally that augmenting a model with eye tracking (ET) data carries linguistic features complementing the information captured by the model and demonstrate that it improves the models' performance. To this end, we will have to collect data from participants and test how different Transformer models benefit from different types of ET attention masks.

Eye movement features are considered to be direct signals reflecting human attention distribution with a low cost to obtain, inspiring researchers to augment language models with eye-tracking (ET) data. In this project, we want to investigate how to operationalise eye tracking (ET) features, such as first fixation duration (FFD) and total reading time (TRT), as the cognitive signals to augment LAI models' attention mechanism (e.g., Transformer attention) during training. We want to demonstrate experimentally that augmenting a model with ET data carries linguistic features complementing the information captured by the model and demonstrate that it improves the models' performance. To this end, we will have to collect data from participants and test how different Transformer models benefit from different types of ET attention masks.

• Try different approaches to incorporating gaze features in the training process of DL/LLM models and compare against baselines/experiments (these should be replicated).

Recent advancements in nanotechnology have enabled the concept of the "Human Intranet", where devices inside and on our body can sense and communicate, opening the door to multiple exciting applications in the healthcare domain. This thesis aims to delve into the computing, communication, and localization aspects of the "Human Intranet" and how to practically realize them in the next decade.

Recent advancements in nanotechnology have enabled the development of means for sensing and wireless communications with unprecedented miniaturization and capabilities, to the point that they can be introduced into the gastrointestinal tract inside a pill or into the bloodstream in the form of passively flowing nanomachines. 

This opens the door to the idea of intra-body communication networks, this is, a swarm of nanosensors inside the human body that use communications to coordinate their actions to sense and localize specific events (lack of oxygen, biomarkers, etc). This can lead to the development of applications such as continuous monitoring of diabetes, detection and localization of cancer micro-tumors, or early detection of blood clots. These possibilities are currently investigated by our team at the N3Cat (www.n3cat.upc.edu).

In this context, we are looking for excellent and self-motivated individuals who are eager to work on developing AI schemes (based on graph neural networks or multi-agent RL) for the detection and localization of events inside of the human body. Data will be gathered with an in-house simulator that integrates mobility models (BloodVoyagerS) and communication models (TeraSim).

Quantum computers promise exponential improvements over conventional ones due to the extraordinary properties of qubits. However, quantum computing faces many challenges relative to the scaling of the algorithms and of the computers that run them. This thesis delves into these challenges and proposes solutions to create scalable quantum computing systems.

Quantum computers promise exponential improvements over conventional ones due to the extraordinary properties of qubits. However, a quantum computer faces many challenges relative to the movement of qubits which is completely different from the movement of classical data. This thesis delves into these challenges and proposes solutions to create scalable quantum computing systems and the algorithms that run within them, following the current European projects at N3Cat (www.n3cat.upc.edu) on scalable quantum computing.

The interested candidate will work in a group of several PhD students and in collaboration with Universitat Politècnica de València, working in ONE of the following areas:

• Study the use of AI to model quantum computers or to assist in the development of architectural methods (qubit mapping, qubit partitioning, qubit routing).
• Study the potential scaling and adaptation of the existing quantum computing architectures to specific quantum machine learning algorithms (QAOA, VQE, others).

This thesis aims to explore the possibilities of the new and less studied variant of neural networks called Graph Neural Networks (GNNs). While convolutional networks are good for computer vision or recurrent networks are good for temporal analysis, GNNs are able to learn and model graph-structured relational data, with huge implications in fields such as quantum chemistry, computer networks, or social networks among others.

Seeing that not all neural networks fit to all problems, and that relational data is present in a wide variety of aspects of our daily life, the main focus of this thesis in N3Cat ( www.n3cat.upc.edu ) and BNN-UPC ( www.bnn.upc.edu ) is to explore the possibilities of the Graph Neural Networks (GNNs), whose aim is to learn and model graph-structured relational data. We are looking for students willing to study the uses, architectures, and algorithms of GNNs. To this end, the candidate will work on ONE of the following areas:

• Uses: Applying GNNs in emerging application areas, including but not limited to (1) electroencephalogram (EEG) analysis for Alzheimer's disease detection, epilepsy classification, motor imagery; (2) acceleration of the compilation of quantum computing algorithms
• Architectures: Investigating ways to accelerate the processing of GNNs in multiple computing platforms (CPU, GPU, accelerators).
• Algorithms: Developing meta-learning data-driven models to estimate the accuracy of a GNN for a given graph, without training, through synthetic graph generation and correlation analysis.

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Master Thesis Offers

Many research groups and institutions which are affiliated with HGS MathComp are offering topics for master theses. The following examples are just a few of over 40 topics available through direct contact with workgroups at IWR and other research centers across campus. Students chose their master thesis supervisors at the end of the 2nd term. The thesis topics are highly individual and will be adapted at the education, knowledge and background of the respective candidate. 

Please use the following list as an introduction to some of the many interdisciplinary topics that are part of this master's program - from geo-informatics to bio-mathematics and from environmental modelling to high performance computing.

Sample topic:  Geometric Multigrid Method on Heterogeneous Hardware [PDF] Research Group Prof. Vincent Heuveline • Engineering Mathematics and Computing Lab (EMCL) Interdisciplinary Center for Scientific Computing (IWR)

Website: https://emcl.iwr.uni-heidelberg.de Contact: Dr. Philipp Gerstner

Sample topic:  Computational Genomics and Research Data Management [PDF] Research Group Dr. Duncan Odom • Division of Regulatory Genomics and Cancer Evolution German Cancer Research Center (DKFZ)

Website: www.dkfz.de/en/regulatorische-genomik/index.php Contact: Dr. Fritjof Lammers

Sample topic: Risk profiles of school children in sub-Saharan Africa: A machine learning approach [PDF] Research Group Prof. Dr. Till Bärnighausen • Heidelberg Institute of Global Health (HIGH), Faculty of Medicine at Heidelberg University

Website: https://www.klinikum.uni-heidelberg.de/heidelberger-institut-fuer-global-health/ Contact: Dr. Sandra Barteit

Sample topic:  Numerical Method for Patch-Wise Update of Inverse Scattering [PDF] Sample topic:  Invertible Neural Networks for solving Inverse Problems [PDF] Research Group Prof. Dr. Jürgen Hesser • Mannheim Institute for Intelligent Systems in Medicine, Faculty of Medicine Mannheim at Heidelberg University

Website: https://www.umm.uni-heidelberg.de/forschung/forschungsschwerpunkte/onkologie/mitglieder/juergen-hesser/ Contact: Prof. Dr. Jürgen Hesser

If you plan to start your master thesis, you should get into contact with possible thesis supervisors. They usually do research in the core track you chose for your studies. All lecturers of modules in the program are open for discussions regarding thesis topics.

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Making Your Thesis a Success

For most students, their final research paper is the first major work written during their degree program. It is much more comprehensive and demanding than term or seminar papers they have written during the course of their studies and, therefore, requires more planning.

Formalities: What regulations apply to the thesis?

Here, you will find information on the regulations governing the writing and submission of your thesis.  Formalities Please take note: These specifications apply for bachelor’s and master’s theses. You can find the regulations applying to the diploma thesis in the §§ of the ADPO (General Academic and Examination Regulations) and the FPSO (Departmental Study and Examination Regulations) of your degree program.

Tips and Tricks

Here, you will find some tips, literature and links we have compiled to help you succeed in writing your thesis. Tips and Tricks

Please note: The recently issued prohibition on the use of special characters in connection with gender-sensitive language in Bavarian schools, universities and authorities relates exclusively to the official tasks of the institutions themselves ; the prohibition does not extend to personal speech, documents, texts, and communication in study and teaching . 

Students are free to decide whether and in what form they want to use gender-sensitive language. The use or non-use of gender-sensitive language has no influence on the assessment of examinations, seminar papers, or theses .

English Writing Center and Schreibberatung German as a Foreign Language

The English Writing Center and the Schreibberatung German as a Foreign Language offer free one-to-one consulting in English or German writing to all members of the TUM community. The Center is staffed by both professional language instructors and student peer tutors, all of whom are native English or German speakers. They help you develop long-term proficiency in English or German writing, while polishing your actual texts in the process.

Having trouble choosing a topic for your Bachelor’s or Master’s thesis? Our Themenbörse posts current thesis topics from across the spectrum of TUM’s academic departments.

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Personal advising sessions with  General Student Advising  by appointment

Monday, 9 a.m. – 12 p.m. Tuesday, 1 p.m. – 4 p.m. (Student Information only) Wednesday, 9 a.m. – 12 p.m. Friday, 9 a.m. – 12 p.m.

Bachelor and Master Theses

We welcome Bachelor and Master students to write their thesis at the Institute for Marketing and Customer Insight. We generally offer students a choice from a variety of topics for bachelor’s or master’s theses.

Students can find the topics in the list below. If we do not have a suitable topic advertised, a proactive application is possible. Please familiarize yourself with the research areas of our professors and PhD students and address your proposal (see questions below) to the person who best fits your research project. Supervision of topics from outside this list is possible only to a limited extent.

Before you begin, please make sure that you fulfill the following requirements:

• Make sure you know and comply with the rules and regulations of the Dean’s Advisory Office concerning your specific program.
• Be ready to start working on your thesis (more than 50% share of work time per week) within the next three months.
• Be willing and actively plan to complete your thesis within nine months after you’ve started your work.

If you fulfill the above requirements, we invite you to continue with the application process.

Most exhibition companies use a very conventional pricing structure (usually based on m2). What more innovative options are there – and which trade fair companies are pursuing such approaches (e.g. price differentiation by location, benefit-oriented pricing, success-oriented pricing, etc.)? How could new pricing models be implemented?

For a master thesis, we offer the possibility of conducting and developing a customer journey analysis for an accounting start up. The customer journey analysis (for several personas) is to be scientifically sound and must consider personas and their usage behavior.

The identity of the company will be revealed to the student conducting the research. Furthermore, the IMC can facilitate contact with the relevant people of the company.

As part of a Master’s thesis, we offer the opportunity to empirically develop a marketing plan for a veterinary company with several locations and specializations as well as an e-shop and a cat hotel. The Master’s thesis offers the opportunity to work on a practical project, where the work should serve as the basis for the implementation of the marketing plan in the company. The marketing plan should deal with marketing instruments and channels and also take into account further ideas such as telemedicine/online consultations, marketing of USPs such as nutritional advice, laparoscopic castration, MRI, etc.

The Master’s thesis should create a scientifically sound, comprehensive concept for the company’s marketing. The identity of the company will be communicated to the student carrying out the Master’s thesis. In addition, contact can be established with the relevant departments of the company via the IMC.

Wie entwickeln sich Messen, Ausstellungen und Events in der Zukunft? Welche Bedeutung werden Sie künftig als Marketinginstrument haben – und in welcher Form (z.B. face-to-face, digital, hybrid)? Wie wird die Messelandschaft (B2B, B2C) der Zukunft aussehen?

Sign-up process:

• Fill out the form below: This form requires you to write a first draft of your outline. To ensure a successful outcome, we ask you to take enough time to do this and follow our guidelines.
• Confirmation e-mail: As soon as you submit your details you will receive a confirmation email.
• Feedback on the Evaluation: You will receive feedback on your outline, including the decision on whether or not we will supervise your thesis. Please bear in mind that we cannot accept all applications since we have limited capacity.
• Appointment: hould you receive positive feedback: meeting & completion of your outline. As a general rule, “feedback” takes the form of an invitation to a personal appointment with your future supervisor. After this meeting, you will revise and resubmit your outline draft along with your filled-out and signed sign-up sheet (see Dean’s Advisory Office web pages) via email to your supervisor. We will print out your registration form, physically sign it and return it to you so that you can submit it to the Dean’s Advisory Office. On request, this can also be done by email.