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networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large language models Statistical learning theory and complexity analysis
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experiments at synchrotron sources. The project is funded by the Chalmers Area of Advance Nano, and involves close collaboration with the groups of Sophie Weber (theory, physics) and Saroj Dash (experiment
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physics and condensed matter theories to address the problem of fracture in complex materials. You will be working with experimental model systems and numerical simulations of materials that exhibit
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of tomorrow and creating novel solutions to major global challenges. Our community is made up of 120 nationalities, 14 000 students, 400 professors and close to 5000 other faculty and staff working on our
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. Investigates the feasibility of applying a wide variety of scientific principles and theories. 4. Maintains substantial knowledge of state-of-the-art principles and theories. 5. Develops advanced analytical
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power grids. In this role, you will combine theory and experimentation to address one of the most critical challenges in modern energy systems, maintaining stability in an increasingly converter-dominated
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Number: 6619987 Postdoctoral Research Associate About the Opportunity This job description is intended to describe the general nature and level of work being performed by people assigned
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We are looking for a postdoctoral researcher who wants to contribute to the development of next-generation frameworks for resilient power grids. In this role, you will combine theory and
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practice as well as in theory. Your primary responsibilities will include: Investigate, develop and validate new models to simulate wind turbine response in HAWC2 at different fidelity levels. Validation
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systems to applications in urban science, planning, and decision support. Qualifications Required: PhD in Computer Science or a related field, with a focus in databases, data systems, theory, or algorithms