191 parallel-computing-numerical-methods positions at Technical University of Munich in Germany
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activities. ________________________________________ Candidate Requirements ✅ PhD degree in Engineering, Computer Science, Systems & Control, Statistics, Computational Physics, Computational Chemistry
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toward generative and multimodal AI methods that connect simulation, perception, and control within large-scale digital twins of urban traffic systems in Munich. The focus lies on advancing semantic
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use of the structural information for structure-based ligand design projects in order to develop prediction methods to identify new food ingredients and flavor modulators. Key Responsibilities • AI
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between the fields of computer sciences and architecture. The focus lies mainly on Building Information Modelling, decision-support methods in urban planning and knowledge-based design methods. As part of
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propose to combine machine learning techniques with formal methods. We will focus on safe reinforcement learning of motion planning problems for autonomous vessels. Motion planning is particularly
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introspective consistency and correlation across layers in the MLS map as further input to the BIM model. Development of methods for automatic or semi-automatic MLS and BIM model updates with minimal input from
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-Euclidean spaces Scalable layout methods and visual analytics for large and dynamic networks Dimensionality reduction and embeddings for visual exploration Interaction design and user-centered evaluation
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Qualifications • You have a degree (Master’s or equivalent) in Civil Engineering or Mechanical Engineering with a strong focus on continuum-solid mechanics and computational methods. • You have experience
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methods to tackle challenges in scientific modeling. Affiliations: Technical University of Munich (TUM) & Helmholtz Munich Niki Kilbertus invites applications for a fully funded PhD position. We’re looking
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(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