24 computational-modelling Postdoctoral positions at Technical University of Munich in Germany
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planning and control algorithms Multi-modal perception techniques (e.g., vision, tactile, force) Machine learning models for physical behavior prediction and manipulation strategy adaptation Real-world
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Position in Numerical Mathematics m/f/d, 100%, 2 years+ As part of the second phase of the DFG funded Priority Programme SPP2311, the Chair for Numerical Mathematics under the leadership of Barbara Wohlmuth
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] Subject Areas: Applied Mathematics, numerical methods, simulation and modelling Appl Deadline: 2025/05/31 11:59PM (accepting applications posted 2025/02/13) Position Description: Position Description
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technologies to fundamental physics questions. The advertised positions will be part of the project “QS-Gauge: quantum simulation of lattice gauge theories”, funded by the Emmy Noether programme of the DFG
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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, but also modeling
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research, developing novel computational models to analyze histopathological and multi-omic data. Opportunity to build a collaborative scientific career in computer science and medical data analysis
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, NeRFs, Diffusion Models, LLMs, etc. PhD and PostDoc Positions in Visual Computing & AI The Visual Computing & Artificial Intelligence Group at the Technical University of Munich is looking for highly
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. The role involves setting up experimental models using Particle Image Velocimetry (PIV) and performing Computational Fluid Dynamics (CFD) simulations to analyze fluid flow and deposition patterns. The data
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to obtain research funding Required Skills & Experience A Ph.D. with excellent academic results in Aerospace Engineering, Mechanical Engineering, Electrical Engineering, Computer Science, Physics, or a
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-)Statistics, (Bio-)Informatics, Computer Science or related disciplines Strong background in modeling multi-modal data (images, tables, text, etc) Understanding of biases and causal inference Experience with