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Field
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algorithms capable of operating in large, partially observed spatial domains to infer efficient, interpretable strategies for estimating archaeological potential, that capture distinct criteria including
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algorithms for fluid-structure interaction solvers What you will do Independently develop numerical methods and codes for fluid-structure interaction of wind-powered ships Carry out and analyse high-fidelity
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Research Fellow (Software, AI & Autonomous Systems) Required Qualifications PhD in Robotics, Computer Science, Artificial Intelligence, Electrical, Computer Engineering, or related disciplines. Key
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at least one of the following fields: approximation of neural networks, convergence of Langevin/MC sampling, analysis and algorithm design of interacting particle systems, operator learning, regularity
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. The Role Faculty of Environment, Science and Economy The successful applicant will contribute to the project NATALIE by developing AI algorithms that integrate data from various sources to better understand
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layers. Within this environment, we will design and evaluate new reinforcement learning algorithms capable of operating in large, partially observed spatial domains to infer efficient, interpretable
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collaborative research in electromagnetic sensor systems, including theoretical analysis, numerical modeling, AI-assisted sensing algorithm development, and experimental validation Qualifications: An earned PhD
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collaborative research in electromagnetic sensor systems, including theoretical analysis, numerical modeling, AI-assisted sensing algorithm development, and experimental validation Qualifications: An earned PhD
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, and delivery, ensuring milestones are met and solutions meet industrial standards for quality and safety. Requirements: PhD in Mechanical Engineering, Nanotechnology, or a closely related discipline
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inference) Algorithmic development for bilevel (or multilevel) optimization Methodological developments in Bayesian statistics and/or decision analysis Application of adversarial risk analysis within security