Biomechanical experiment on soccer players (both female and male) in lab settings, to study the effect of an insole on muscular activity, kinematics, kinetics. To help reduce non-contact injuries in football Link to the thesis soccer_thesis.

Bulb mites show a dramatic male dimorphism, developing either into deadly fighters or harmless scramblers, raising the question of why the ill-equipped scramblers still persist in nature. This project explores whether scramblers escape aggression from fighter males by chemically mimicking females, using detailed analyses of their surface chemical profiles. By linking behaviour and chemistry, you will uncover the mechanisms that maintain these surprising alternative tactics.

Modern computer-assisted surgery relies increasingly on robust visual understanding of the operating room. While most existing approaches for surgical scene understanding and phase recognition are based on narrow-field-of-view endoscopic or monocular camera data, real-world operating rooms often provide significantly richer visual contexts. This thesis aims to investigate surgical scene understanding using multiple images for surgical phase recognition, and higher-level scene representations such as surgical scene graphs. The goal is to analyze how VLM/multi-view VLMs can use the visual context can improve robustness, generalization, and interpretability. The project will commence with a comprehensive review of the state-of-the-art in surgical activity recognition, surgical phase classification, and scene graph–based modeling. Based on this review, existing methods will be categorized and experimentally evaluated, with adaptations made where necessary to support panoramic or wide-field-of-view imagery. Particular emphasis will be placed on testing and validating approaches on recordings beyond classic benchmark datasets, including less constrained or previously unseen surgical recordings. The student is encouraged to bring in their own ideas and creativity. The outlined approach serves as a guiding framework rather than a strict implementation plan. Your own creativity is welcome to solve the problem; the upper proposal merely presents an idea. Please read the application details: https://hex-lab.io/vacancies/01_students/ Key research areas include: Reviewing the state-of-the-art in surgical activity / phase classification techniques and surgical scene graph techniques Categorize and experiment with existing approaches Test on recordings aside from classic datasets Recommended background (or motivation in learning): Basic knowledge of computer vision Experience with deep learning model training and application Some experience with C++ and Python Medical Context: - https://github.com/egeozsoy/ORacle Multi-View VLMs: - InternVL https://github.com/OpenGVLab/InternVL - LLaVA-NeXT as well: https://arxiv.org/abs/2407.07895; https://github.com/LLaVA-VL/LLaVA-NeXT Fine-tuning VLMs for 3D vision tasks: - https://github.com/openvla/openvla - https://roboflamingo.github.io/ - https://physx-anything.github.io/

Deep Reinforcement Learning (DRL) enables robots to learn optimal control strategies through interaction with their environment. Current research focuses on sample-efficient learning in the real world, finetuning foundation models, and sim-to-real transfer. We offer several thesis topics in these areas. Please find all information and the thesis announcement here: https://nextcloud.in.tum.de/index.php/s/2N6xY7L7pqC5rP3

This project aims to improve boring exercises with real-time motion tracking and gamification approaches in combination with musculoskeletal simulations to get meaningful outputs.

Have you ever dreamed of working with cells that are easy to cultivate, resistant to contamination, and can always be observed in vibrant colours under a microscope? Would you like to join us on this international adventure between the Politecnico di Milano and the TUM Campus Straubing (All TUM students are welcome and eligible!), with the opportunity to complete your Master’s thesis in Italy (ERASMUS+)? (Read more for the description of the project!)

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This thesis aims to investigate surgical scene understanding using panorama imaging, with a particular focus on semantic segmentation, surgical phase recognition, and higher-level scene representations such as surgical scene graphs. The goal is to analyze whether panoramic visual context can improve robustness, generalization, and interpretability compared to conventional camera setups.

The Leibniz Institute for Food Systems Biology at the Technical University of Munich (LeibnizLSB@TUM) is a prominent member of the Leibniz Association and the research group Mechanoreceptors / Section II is currently looking for a committed Master’s Student of Biophysics/ Biochemistry/ Food Chemistry/ or related for the preparation of a Master's thesis to start in Summer/Fall 2026.

Master thesis project: We invite applications for a Master’s thesis position at the German Aerospace Center (DLR), Institute of Robotics and Mechatronics, focusing on space robotics control.

You are passionate about applying cutting-edge information technology to solve the energy and climate crisis and would like to work in a vibrant research environment? Then let’s design the energy systems of the future together!

This Master’s thesis focuses on the analysis, modeling, and interpretation of high‑resolution climate data from forest edge environments. Using long‑term measurements of temperature, moisture, and radiation, the work aims to identify and quantify microclimatic patterns related to different forest edge structures.

How does human posture change when drill height, tool design or applied force vary? Find out – using real biomechanical data from the CoSiMMI research project! In this thesis you will develop an AI model that predicts realistic working posture form joint angles, EMG, grip forces and force-plate data. Your work will help design more ergonomic tools and explore how AI van truly understand human movement. Interested in machine learning, biomechanics and real-world data? Join us! Requirements: Interest in biomechanics, ergonomics, or AI Basic skills in Python or MATLAB First experience with machine learning is an advantage Enjoyment of data analysid and modeling The project can also be completed as a team – feel free to encourage interested friends or fellow students to join and apply together. For more insights into the project please contact me.

The Chair of Robotics, Artificial Intelligence, and Real-Time Systems offers a Master’s thesis on synchronization challenges in V2X cooperative perception systems based on ETSI CAM and CPM communication standards.

Master’s Thesis: Adjoint Methods for Nonlinear Dynamic Problems: Expanded vs. Non-Expanded Formulations Background and Motivation: Inverse problems in nonlinear structural and cardiac mechanics require efficient and accurate gradient computations. For time-dependent problems, adjoint methods become challenging due to the path dependence introduced by time integration schemes such as generalized-α. A common approach in the literature expands the system by including time-integration variables, leading to a larger coupled system (≈ 3× number of degrees of freedom) [1,2]. While this avoids complex recursive derivatives, it requires specialized solvers and preconditioning strategies. An alternative approach avoids this expansion by analytically resolving the recursion and keeping the problem size unchanged. The relative advantages of these two fundamentally different strategies are currently not well understood. Objective: The objective of this thesis is to implement and analyze the expanded adjoint formulation for nonlinear dynamic problems based on generalized-α time integration, and to systematically compare it with an existing non-expanded (same-size) adjoint approach already developed within the research group. Building on the group’s prior work and available implementation of the non-expanded method, the student will focus on implementing the expanded formulation from the literature, optimizing both approaches where necessary, and evaluating their performance in terms of accuracy, computational efficiency, numerical robustness, and solver and preconditioning requirements. Although the topic is methodologically challenging, the work will be closely supervised and supported. Tasks: Literature review Study adjoint methods for time-dependent nonlinear problems Understand expanded (augmented) vs. non-expanded formulations Implementation of expanded formulation Implement the adjoint system with expanded unknowns (~3× ndof) Assemble the corresponding linear systems Solver and preconditioning strategy Identify and test suitable iterative solvers Design and evaluate preconditioning strategies for the expanded system Comparison study Compare expanded vs. non-expanded approaches with respect to: computational cost implementation complexity Analysis and conclusions Identify the advantages and limitations of both approaches Provide recommendations for practical applications Qualifications: Strong background in applied mathematics, numerical methods, or computational mechanics Solid understanding of finite element methods and linear algebra Basic knowledge and interest in iterative solvers and preconditioning Very good programming skills in C++ Interest in advanced topics such as adjoint methods and inverse problems Supervisors: Prof. Dr.-Ing. Michael Gee; Tahar Arjoune, M.Sc. References: [1] Alberdi R, Zhang G, Li L et al (2018) A unified framework for nonlinear path-dependent sensitivity analysis in topology optimization. Int J Numer Meth Eng 115(1):1–56. [2] Arjoune, T., Bilas, C., Meierhofer, C. et al. Inverse analysis of patient-specific parameters of a 3D–0D closed-loop cardiovascular model with an exemplary application to an adult tetralogy of Fallot case. Biomech Model Mechanobiol 24, 2039–2068 (2025).

Externe Masterarbeit oder Forschungspraxis bei Sureel.ai .

Are you curious how your knee feels during all the things you do throughout the day? This project aims to reconstruct and improve a semi-automated biomechanical simulation pipeline.

Jointly with Occurrence AB — a dynamic startup based in Stockholm and Munich — Chair of Perception for Intelligent Systems offer several MSc thesis topics concerning human activity analysis and digital twins in real production environments.

We are offering an exciting opportunity for a highly motivated student to investigate a novel therapeutic approach based on programmable RNA editing for the treatment of Beta-propeller Protein-Associated Neurodegeneration (BPAN).

We are seeking a highly motivated student to contribute to the implementation and evaluation of real-time signal processing functionalities on FPGAs.