Technical University of Munich, Germany invites online Application for number of Fully Funded PhD Degree at various Departments. We are providing a list of Fully Funded PhD Programs available at Technical University of Munich, Germany.
Eligible candidate may Apply as soon as possible.
(01) PhD Degree – Fully Funded
PhD position summary/title: PhD student (m/f/d)
This research project addresses the profound impacts of environmental pollution on human health, specifically targeting how air- and foodborne pollutants may impair taste perception and nutrient sensing. The project aims at clarifying whether exposure to pollutants compromises sensory functions, potentially affecting appetite regulation, caloric intake, and overall health outcomes. To explore this hypothesis, we will integrate a combination of mechanistic in-vitro studies and population-based epidemiological research. Researchers on this project will benefit from a multidisciplinary environment at the intersection of cellular biology, toxicology, and epidemiology. As part of our team, you will collaborate with leading experts in environmental health, stem cell technology, and epidemiology. This collaborative environment fosters innovation and skill development, providing hands-on training in organoid culture, pollutant exposure methods, and data analysis. Additionally, through a planned cohort study, you will gain experience in translating laboratory findings into real-world health applications.
Deadline : Open until filled
(02) PhD Degree – Fully Funded
PhD position summary/title: PhD position (m/f/d) Microclimate and phenology in mountain forests
You will conduct research on ecoclimatological conditions and phenology in different mountain forest ecosystems (e.g., National Park Berchtesgaden, region Werdenfelser Land) from in-situ experimental data to the landscape scale. Doing so, you will address questions of climate change impacts on meteorological extremes, phenology of selected forest tree and animal species and possible adaptation strategies. Specifically, tasks include:
- Upgrading and running of meteorological instrumentation, wildlife cameras and dendrometers in the field
- Combining field data for climate change impact and adaptation analyses, and for spatio-temporal modelling as well as upscaling of ecosystem properties via remote sensing
- Interactive collaboration and exchange within the TUM Center for Forest Management in the Alps
- Publication of peer-reviewed scientific papers in international journals
- Communication of research findings at scientific conferences and stakeholder meetings
Deadline : 05 October 2025
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(03) PhD Degree – Fully Funded
PhD position summary/title: PhD (f/m/x) in Machine Learning for Scientific Modeling
Our group operates across the Helmholtz Munich Campus [maps] and the TUM Garching campus [maps], and all members are affiliated with both institutes. As a PhD candidate in our group, you will drive your own research on machine learning methods in close collaboration with the other group members including Niki and external collaborators. Niki’s primary goal is to empower you to become a creative, independent researcher, ready to lead your own group in the future. Hence, you will have substantial freedom to define projects.
We focus on methods-driven ML for scientific modeling, currently emphasizing the integration of data-driven and mechanistic approaches for dynamical systems, causality, and ML for science more broadly. We publish at core ML venues and are also interested in collaborating on applications in biomedicine, climate, or physics. Find out more at nikikilbertus.info.
Deadline : Open until filled
(04) PhD Degree – Fully Funded
PhD position summary/title: Doctoral Research Associate in Generative Multimodal Recommender Systems
Recommender systems are a cornerstone of modern digital platforms, but traditional approaches like matrix factorization struggle with significant limitations, including the cold-start problem, static user representations, and data sparsity. While deep learning models offer improvements, they often come with high computational costs and require frequent retraining, which limits their scalability and adaptability.
This research project aims to pioneer the next generation of recommender systems by moving beyond static methods. The core of this PhD is to develop a generative multimodal recommender system that leverages pretrained multimodal encoders (like CLIP) and advanced sequential modeling techniques. The central hypothesis is that a user’s preferences are encoded in the sequence of items they interact with. By representing items through rich multimodal embeddings (from images and text) and modeling user behavior as a sequence, the system can dynamically adapt to new content and users without constant retraining. This project will be conducted in close collaboration with our industrial partner, Audi, focusing on real-world application scenarios, such as recommending Points of Interest (POIs).
Deadline : Oct 3, 2025
(05) PhD Degree – Fully Funded
PhD position summary/title: PhD Position in Marketing Analytics
Our Marketing Analytics team focuses on the quantitative-empirical analysis of consumer and business behavior, the impact of new technologies on this behavior, as well as the resulting implications for consumer welfare. Our research is interdisciplinary and based on collaboration with the fields of Information Systems, Finance, and Psychology.
Deadline : Open until filled
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(06) PhD Degree – Fully Funded
PhD position summary/title: Ph.D Position on Machine Learning in Biomechanics (m/w/d)
A Ph.D. position is available in the Professorship of Data-driven Materials Modeling to contribute to the development of efficient, certifiable, and practical solutions for inverse problems in diagnostic biomechanics.
A Ph.D. position is available in our group to contribute to the development of efficient, certifiable, and practical solutions for inverse problems in diagnostic biomechanics, with a particular focus on elastography. The project builds on our recently developed Weak Neural Variational Inference
(WNVI) framework and aims to advance its capabilities in the following directions:
• Scalability: Extending methods to high-dimensional and three-dimensional elastography
problems using neural operator representations.
• Computational efficiency: Designing adaptive and physics-aware strategies (e.g., optimized residual selection, physics-based zooming) for real-time inference.
• Practical usability: Developing robust, user-friendly frameworks for multimodal elastography and enabling deployment on portable devices (e.g., smartphones) for real-time diagnostics.
The research combines continuum mechanics, machine learning, computational mathematics, and probabilistic modeling, with direct applications in medical imaging and beyond.
Deadline : Open until filled
(07) PhD Degree – Fully Funded
PhD position summary/title: PhD or Postdoctoral Position in Numerical Analysis
The advertised position is a project-independent research position without teaching responsibilities. It allows for the autonomous selection of a specific research topic from the areas represented within the group. The group’s interdisciplinary focus includes not only classical topics in numerical analysis, such as the analysis of nonlinear PDEs or the development of new solver- or coupling-methods including their convergence analysis, but also modeling and simulation aspects across a wide range of fields – from biomechanics and geophysics to polymer-fluid coupling. Further areas of interest include numerical algorithms for high-dimensional problems, classical (mainly finite elements) as well as alternative discretization methods (e.g., Lattice Boltzmann Methods), and high-performance computing
Deadline : October 17, 2025
(08) PhD Degree – Fully Funded
PhD position summary/title: Doktorand (m/w/d) (TVL E13-100%) im Bereich „Bias in Large Language Models“
Das Projekt entwickelt neuartige Methoden zur Vorhersage der Leistung von LLMs, zur Entwicklung von Routing-Strategien in föderierten Multi-LLM-Umgebungen und zur Minderung politischer Bias durch die Aggregation verschiedener Modellausgaben. Ziel ist es, fairere und transparentere KI-Systeme zu entwickeln. Das Projekt wird in enger Zusammenarbeit mit der University of Queensland (Australien) durchgeführt und bietet spannende Möglichkeiten für internationale Forschungsaufenthalte.
Deadline : 30. September 2025.
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(09) PhD Degree – Fully Funded
PhD position summary/title: Doktorand (m/w/d) UCN
Ihr Aufgabengebiet:
- Sie erforschen die Reflexion und Speicherung von ultrakalten Neutronen an neuartigen Oberflächenbeschichtungen aus ultra-nanokristallinem Diamant
- Weiterhin führen Sie Streuexperimente von ultrakalten Neutronen an Kryo-Festkörpern durch, um die Wechselwirkungsquerschnitte bei niedrigsten Energien zu bestimmen
- Ihre experimentell gewonnenen Ergebnisse bewerten Sie im Hinblick auf Auslegung und Verbesserung von neuartigen Quellen für sehr kalte und ultrakalte Neutronen auf Basis von superthermischer Konversion
- Ihre Experimente führen Sie an verschiedenen Forschungseinrichtungen (z.B. FRM II, TRIGA Mainz, ILL Grenoble) durch
- Das Promotionsverfahren wird an der TUM School of Natural Sciences der Technischen Universität München durchgeführt
Deadline : 30. September 2025.
(10) PhD Degree – Fully Funded
PhD position summary/title: Ph.D Position on Machine Learning in Biomechanics (m/w/d)
A Ph.D. position is available in our group to contribute to the development of efficient, certifiable, and practical solutions for inverse problems in diagnostic biomechanics, with a particular focus on elastography. The project builds on our recently developed Weak Neural Variational Inference
(WNVI) framework and aims to advance its capabilities in the following directions:
• Scalability: Extending methods to high-dimensional and three-dimensional elastography
problems using neural operator representations.
• Computational efficiency: Designing adaptive and physics-aware strategies (e.g., optimized residual selection, physics-based zooming) for real-time inference.
• Practical usability: Developing robust, user-friendly frameworks for multimodal elastography and enabling deployment on portable devices (e.g., smartphones) for real-time diagnostics.
The research combines continuum mechanics, machine learning, computational mathematics, and probabilistic modeling, with direct applications in medical imaging and beyond.
Deadline : Open until filled
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(11) PhD Degree – Fully Funded
PhD position summary/title: PhD/Postdoc Position for Safe Navigation of Autonomous Ships
Maritime trade is of central importance for global trade. Much of it is based on the transportation of goods by large container ships. These are not very flexible due to fixed routes, cause environmental problems and are prone to serious and costly accidents due to human error. Solutions to these problems, such as sustainable drives and autonomous navigation, are not yet fully developed and are still at the experimental stage. To address these issues, the company CargoKite (https://cargokite.com/) is developing a wind-powered ship for autonomous, highly flexible global container transportation, which is currently at the concept stage. This is a transport ship with kite-based propulsion and an additional diesel engine to bridge port trips and wind lulls.
In this project, we develop with CargoKite a new type of collision avoidance and control system for real-time control and operation at autonomy level 3 as defined by the International Maritime Organization (IMO 1). The control system to be developed will almost completely avoid collisions and maximize efficiency through innovative AI-based movement and maneuver planning. For the first time, innovative machine learning concepts, such as “shadow learning”, are being used. Appropriate interfaces are also being developed for the new control system so that, in addition to its use in the CargoKite container ship, a stand-alone package is created that can also be used for other wind-powered and purely motor-powered ships and will be marketable as an independent product. The new system to be developed will be tested and optimized with an existing CargoKite ship prototype in real operation.
Deadline : 30 September 2025
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(12) PhD Degree – Fully Funded
PhD position summary/title: PhD Position in Transportation Analytics
The successful candidate will conduct research on data-driven modeling of transportation systems, using techniques such as:
- High-dimensional data mining
- Tensor decomposition
- Causal inference
- Statistical process modeling
- Machine Learning
Deadline : September 30, 2025
(13) PhD Degree – Fully Funded
PhD position summary/title: Doctoral Research Associate – Chair of Strategic and International Management (m/f/d)
Our research addresses strategies, transformations, and firm success. We strive to give success-critical impulses to business practice and further develop strategy concepts and theories. We focus on three areas: strategy&technology (in particular deep tech such as AI, robotics, biotech), strategy&global contexts, and the theory of the firm (see www.msl.mgt.tum.de/simanagement) We do not only cooperate with renowned scholars at home and abroad but also with well-known corporate partners. Research results are presented at international conferences as well as transferred to business practice.
Previous doctoral candidates of Professor Hutzschenreuter are today executives in multinational enterprises, international strategy consultancies, and private equity companies and work for renowned business schools.
Professor Hutzschenreuter intensively supports you on your research project. You will work along a structured project plan with clear milestones for each semester. As part of the doctoral studies at TUM Graduate Center (Link) you will learn the methods of academic research and have access to opportunities for content-specific postgraduate courses.
Deadline : Open until filled
(14) PhD Degree – Fully Funded
PhD position summary/title: PhD Position in Theoretical Algorithms or Graph and Network Visualization – Promotionsstelle (m/w/d)
We seek PhD students with strong theoretical foundations and a desire to contribute to fundamental algorithmic research. Our group works at the intersection of algorithms, machine learning, and interactive visual interfaces. Topics of interest include:
- Planar and geometric graph algorithms
- Approximation and parameterized algorithms
- Clustering, embeddings, and structural graph theory
- Computational complexity and efficient algorithms -Graph drawing and layout algorithms (including dynamic and high-dimensional visualizations)
- Visualization in non-Euclidean spaces (e.g., spherical or hyperbolic)
- Perceptual studies and evaluation methods
- Interactive systems for exploring complex networks
Deadline : Open until filled
(15) PhD Degree – Fully Funded
PhD position summary/title: Ph.D. Student / Junior Researcher Microeconomics / Applied Economics
The successful candidate will conduct cutting-edge empirical research in at least one of the following areas
- natural resources and environment,
- climate change and policy,
- digitalisation, AI and smart technologies in the bioeconomy,
- agricultural, food and environmental policy impact analysis, and/or
- production efficiency and food markets.
Deadline : Open until filled
About Technical University of Munich, Germany –Official Website
The Technical University of Munich (TUM or TU Munich; German: Technische Universität München) is a public research university in Munich, Germany. It specializes in engineering, technology, medicine, and applied and natural sciences.
Established in 1868 by King Ludwig II of Bavaria, the university now has additional campuses in Garching, Freising, Heilbronn, Straubing, and Singapore, with the Garching campus being its largest. The university is organized into seven schools, and is supported by numerous research centers. It is one of the largest universities in Germany, with 52,580 students and an annual budget of €1,839.2 million including the university hospital.
A University of Excellence under the German Universities Excellence Initiative, TUM is among the leading universities in the European Union. Its researchers and alumni include 18 Nobel laureates and 24 Leibniz Prize winners.
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