78 phd-agent-based-modelling PhD positions at Technical University of Munich in Germany
Sort by
Refine Your Search
-
? Our current research is based in India, Rwanda, and Uganda, but we are always seeking ways to learn about and work in new places! You can find out more about us here. About the PhD Researcher Position
-
samples provided by other team members. Contribute to the teaching activities of the group. We offer A fully DFG-financed PhD-position for three years on a 67% basis (27 h per week, E13 TV-L). Severely
-
hydrodynamic models and 3D urban semantic models in collaboration with project partners, with applications to recent major flood events in Germany, Brazil, and Spain. Supervision & collaborations: The PhD will
-
research group Metabolic Function & Biosignals (Section II) is currently looking for a committed PhD Student to start as soon as possible. PhD Student (m/f/d) The Leibniz Institute for Food Systems Biology
-
𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻 The PhD focuses on safe and reliable loco-manipulation for mobile and interactive robots such as humanoids. While learning-based methods enable advanced locomotion and manipulation, deploying them
-
to fill a full-time PhD position in Marketing Analytics PhD Position in Marketing Analytics, starting as soon as possible for an initial term of three years. Research focus Our Marketing Analytics team
-
analytical skills for model formulation and optimization Demonstrated research potential, ideally with a track record of publications in relevant venues (journals such as IEEE T-ITS, INFORMS Transportation
-
(m/f/d) in the topic: “AI-based processing of CAD models for automated planning of computer-aided manufacturing.” The candidate has the opportunity to pursue a doctoral degree (Ph.D.). Remuneration is
-
01.10.2025, Wissenschaftliches Personal Join the new EU-funded FutureForests international doctoral network and undertake an interdisciplinary PhD project with the Plant Ecophysiology group
-
regimes using simulation modeling Quantification of climate change impacts on protective forests (scenario analysis) Contributions to evidence-based management decision making in mountain forests