Sort by
Refine Your Search
-
07.09.2025, Wissenschaftliches Personal We are seeking a PhD or Postdoctoral researcher to join the AI in Orthopaedics group (TUM & TUM University Hospital). The position focuses on developing
-
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
-
Iterative Algorithms: Optimization and Control.” About the Project The focus of the project is the analysis of iterative algorithms arising from time discretizations of nonlinear evolutions of various kinds
-
data analysis and develop sophisticated mathematical models for simulating power system behaviors under various scenarios. Development and Testing: Design and develop control algorithms to enhance grid
-
algorithmic algebra. For more information about the TUM Department of Mathematics, please visit our website: https://www.math.cit.tum.de/en/math/home/. The position is a full-time position (100%), initially
-
research studies for automated image analysis. In particular, you will: Plan, develop, and implement AI/ML algorithms for pathology image analysis. Integrate multi-modal data (e.g., genomics, clinical data
-
on the design and evaluation of innovative data- and machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization
-
energy use more efficient. We develop new optimization methods, machine learning algorithms, and prototypical systems controlling complex energy systems like electric grids and thermal systems for a
-
MesaPD to solve complex multiphysics problems. The coupling is done across package boundaries. This also requires more sophisticated approaches in load-balancing. Finally, the newly developed algorithms
-
the possibility of an extension. TASKS: Mathematical modeling and development of inverse methods (e.g. Bayesian inversion, optimization based methods, sparsity promoting methods based on L1-norm minimization and