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postgraduate levels. We welcome applications from candidates whose research aligns with any area of Artificial Intelligence, including but not limited to Mathematical Theories for AI: Algorithmic Optimizations
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postdoctoral researcher with a solid background in one or more of the following areas: game theory, optimization algorithms, and numerical methods. The successful candidate will develop efficient algorithms and
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across multiple da-tasets, domains, or tasks Is familiar with mathematical optimization methods, such as mixed-integer linear programming Has experience with uncertainty quantification, surrogate modelling
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experience with tabular or heterogeneous data, particularly across multiple da-tasets, domains, or tasks Is familiar with mathematical optimization methods, such as mixed-integer linear programming Has
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funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The postdoctoral researcher will
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Assistant Professor in Data Science Posting Number req37719 Employment Type Faculty Faculty Type Tenure/Tenure-Track Hiring Department Mathematics Statistics (869A) Academic Location College of Arts
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, learning to optimize, large models, AI Agents, and automatic differentiation. 2. Qualifications Assistant Professor A Ph.D. in applied mathematics, operations research, computational mathematics
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Previous Job Job Title Postdoctoral Associate in Mathematical Foundations of Generative AI Next Job Apply for Job Job ID 375806 Location Twin Cities Job Family Academic Full/Part Time Full-Time
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engineering, control engineering, operations research, applied mathematics, or a closely related field. Documented experience in energy-system modelling, mathematical optimization, control, digital twins
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. Your work tasks This two-year postdoctoral project focuses on the system-level integration and optimization of electrolyzer digital twins within CAPeX — considering how component-level electrolyzer