15 algorithm-sensor-"University-of-Manchester" PhD scholarships at Technical University of Munich
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21.12.2021, Academic staff The Department of Computer Science, Technical University of Munich, has a vacancy for a PhD candidate/researcher position in the area of efficient algorithms. The position
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infrastructure, including ASCENT, our vertical takeoff and landing (VTVL) hopper with a bi-liquid propulsion system, currently in development. One of our research branches focuses on intelligent algorithms
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reinforcement learning methods can be used to solve multiobjective discrete and combinatorial optimization problems. The goal is to develop new algorithmic approaches that combine ideas from machine learning
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validation of linear-scaling electronic-structure and optical-response methods. This includes the advancement and use of efficient algorithms, benchmarking against established approaches, and application
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predict food-effector systems. Key Responsibilities • Develop graph-based (multi-)omics analysis algorithms • Benchmark graph-theoretic against graph-ML approaches • Analysis of food-related (multi-)omics
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-centered control paradigms. Design and implement algorithms for shared control between human operators and autonomous systems to improve safety, transparency, and performance in teleoperation. Implement and
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their thesis work in the field of robotics; Strong programming skills in C++ and/or Python, as well as experience in implementing robot learning algorithms; A strong background in control theory, machine
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network of collaborators at the University of Queensland, across Europe (e.g. Lancaster University, The University of Manchester, Wageningen University, University Giessen, Göttingen University, GFZ Potsdam
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05.04.2023, Academic staff We are the Autonomous Vehicles Systems (AVS) Lab and are interested in the algorithmic foundations of path and behaviour planning, control and automated learning
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05.04.2023, Academic staff We are the Autonomous Vehicles Systems (AVS) Lab and are interested in the algorithmic foundations of path and behaviour planning, control and automated learning