26 agent-based-modelling Postdoctoral research jobs at Technical University of Munich in Germany
Sort by
Refine Your Search
-
). Employment Conditions • Start date: Flexible, from January 2026 onward • Salary: Based on the German public sector pay scale TV-L E13, commensurate with experience and qualifications. • Generous funding
-
machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization methods, machine learning algorithms, and
-
. The aim of our group is to improve the understanding of the trade-offs between production, mitigation and conservation in livestock-based systems, and to identify innovative mechanisms for landscape-level
-
, and high-performance computing. It aims to improve the performance of the matrix-free finite-element-based framework HyTeG, in particular by techniques for data reduction through surrogate operators
-
01.07.2025, Wissenschaftliches Personal The position is based within the research group of Deniz Kus, Professor for Representation Theory at the Department of Mathematics, part of the TUM School
-
the understanding of the trade-offs between production, mitigation and conservation in livestock-based systems, and to identify innovative mechanisms for landscape-level management. Our group combines empirical work
-
. The position is based within the research group of Deniz Kus, Professor for Representation Theory at the Department of Mathematics, part of the TUM School of Computation, Information and Technology (CIT
-
materials science • Extensive knowledge of computer-based modelling and simulation methods in materials science of metals, e. g. Calphad method, precipitation simulation, cellular automata, kinetic Monte
-
focus on deep networks for solving inverse problems, learning robust models from few and noisy samples, and DNA data storage. The position is in the area of machine learning, with a focus on deep learning
-
communication system are modeled using information theory. We wish to investigate how interleaving can reduce the overhead and computational load due to coding coefficients required in classical linear random