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Field
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learning, optimization, mathematical foundations of artificial intelligence, mathematical ecology and mathematical statistics. Qualifications To qualify as a postdoctoral scholarship holder, the postdoctoral
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materials using finite element technique What you will do As a postdoc in our group, you will lead research on the design and implementation of mathematically sound methods for the topology optimization
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record to contribute to the development and automation of advanced electrolysis systems. The successful candidates will work on the automation, operation and optimization of electrochemical systems
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engineering Mathematics » Applied mathematics Mathematics » Computational mathematics Researcher Profile Recognised Researcher (R2) Application Deadline 15 Oct 2026 - 21:59 (UTC) Country Sweden Type of Contract
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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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energy-system modelling, mathematical optimization, control, digital twins, or related areas. Strong programming and numerical modelling skills using Python, MATLAB/Simulink, Julia, hardware in the loop
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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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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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candidate will contribute to cutting-edge research in optimal control theory, differential game theory, mean-field game theory, and mean-field-type game theory, developing advanced mathematical frameworks and
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journals and presentations at conferences. - Collaboration with interdisciplinary research teams as part of the CERTAINTY project. - Optimization and documentation of modeling configurations for future