49 molecular-dynamic-simulation PhD positions at Technical University of Munich in Germany
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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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adaptive, automated decision-making tools (e.g., traffic signal control, human–vehicle coordination, logistics optimization, route planning) using reinforcement learning in dynamic environments. Explanation
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for dynamical systems, causality, and ML for science more broadly. We publish at core ML venues and are also interested in collaborating on applications in biomedicine, climate, or physics. Find out more at
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-selectivity. Calculations and simulations will guide fabrication of experimental prototypes, to be tested in beamtime experiments at world-leading neutron science facilities (e.g. ILL, FRM II). Experimental
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Master’s students, supporting their academic and research development. ▪ You will be part of a dynamic international team dedicated to advancing quantum technologies and computing with superconducting qubits
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PhD Position in Theoretical Algorithms or Graph and Network Visualization - Promotionsstelle (m/w/d)
efficient algorithms -Graph drawing and layout algorithms (including dynamic and high-dimensional visualizations) Visualization in non-Euclidean spaces (e.g., spherical or hyperbolic) Perceptual studies and
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all over Germany join forces to integrate a thermodynamic description into the processes understanding of soil systems and gain insights into how energy and matter fluxes drive organic carbon dynamics
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uncertainty from environment perception and your own state estimation, and then integrating it into a newly developed trajectory and behavior planner. The goal is to enable safe, reliable and highly dynamic
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and regulatory compliance for innovative NLP applications. Full-time position (100% TVL-E 13). A collaborative environment in a dynamic, young team at one of Europe’s top universities. Opportunities
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Signals in Cancer” (Head: Prof. Dr. med. Jürgen Ruland) is to (i) explore the molecular mechanisms and pathways that drive immune dysfunction and immunotherapy resistance, (ii) modulate cancer