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AI-driven decision making. It will be conducted in collaboration with the Automatic Control group (from Department of Electrical Engineering) and the Mathematical Optimization group (from Department
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-precision hardware without compromising simulation fidelity. Data-centric optimization: Designing and implementing communication schemes and data access strategies targeting post-exascale systems, e.g., using
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of navigating extremely large compositional spaces while simultaneously optimizing multiple target properties such as optical performance, stability, and sustainability. Who we are looking for We seek a highly
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engineering, industrial engineering, applied physics, or similar). This eligibility requirement must be met no later than the time the employment decision is made A solid background in dynamic systems, optimal
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platform to identify and optimize therapeutic candidates. Our group specializes in developing technologies to assess the multicellular environment within three-dimensional microtumor models. The project
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gasification and pyrolysis Good programming skills in MATLAB, Aspen Plus, Modelica, etc Good knowledge of data driven methods for modelling and optimization Good knowledge of AI Experiences in scientific
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postdoctoral position focused on innovative research in coupled transport-energy modeling and optimization. We are looking for a postdoc to join our team. Become part of our innovative group and contribute
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must be met no later than the time the employment decision is made. At least three of: (stochastic) partial differential equations ((S)PDEs) graph theory/network science stochastic optimization and
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robotics – reinforcement learning, whole-body model predictive control (MPC), and differentiable optimal control – to simulate human balance and step recovery in urban transport scenarios. The goal is a
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intelligence - Data-driven and learning-based control - Decentralized decision-making and distributed optimization - Belief-space and uncertainty-aware planning - Neuro-symbolic and context-aware reasoning