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in the chip design process Optimization and Reinforcement Learning methods for problems of design/layout Exploration of Heuristics and Data-driven Methods for the generation of design blocks Automated
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to the answer to this question is an intelligent, central control unit that orchestrates future autonomous vehicle fleets, optimizes road traffic, clears the road in the event of a disaster, switches traffic
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Autonomous Challenge or EDGAR . Thereby, we research current problem fields in the areas of perception, planning, control, safety and evaluation. Our goal is always to develop the optimal overall software
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technology constraints Placement & Routing for technology-independent layout generation Clocking and data synchronization Layout validation and verification Technology mapping and optimization To learn more
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efficient. We develop new optimization methods, machine learning algorithms, and prototypical systems controlling complex energy systems like electric grids and thermal systems for a sustainable future. These
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Observation Data For HPDA/HPC: Experience in setup, management, and user support of HPDA computing systems; experience in optimizing algorithms for HPC systems For Education: Teaching/training experience in
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on superconducting quantum computers Developing algorithms to decompose (arbitrary) unitaries into native operations of a given target system Optimizing circuits taking error models of actual hardware into account
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will need strong coding skills to design highly efficient algorithms. Solid knowledge in the areas of algorithmics, optimization problems, as well as experience with SAT/SMT solvers or machine learning