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Field
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Responsibilities: Conduct programming and software development for data management. Design and implement machine learning models for optimizing data management. Conduct experiments and evaluations of the designed
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system performance in physiologically relevant settings. Explore and implement AI/ML tools for system optimization, data-driven control, or high-throughput analysis. Contribute to publications, IP filings
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compatibility analysis of materials for different components Mechanical characteristics evaluation and optimization Support mechanical and chemistry relevant qualification and failures analysis Drive specific
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photocatalytic degradation of Per- and Polyfluoroalkyl Substances (PFAS) with optimized COFs Look for necessary collaborations with internal or external collaborators (e.g. theoretical modelling and advanced
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. Develop and enhance advanced optimization algorithms for the Energy Management System (EMS), addressing energy dispatch, storage control, load scheduling, and strategies for market participation. Architect
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processes and develop innovative tools in cell biology. Key Responsibilities: Conduct experimental research using quantitative biochemistry, biophysics, and cell biology. Develop and optimize advanced
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the industry to lead and/or conduct innovative research on, but not limited to evolutionary computing, job scheduling, transfer optimization, transfer learning, reinforcement learning, large-scale
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. No one can be appointed for more than one PhD Research Fellowship period at the University of Oslo. Job description The person hired in the position will work on theoretical control and optimization
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of Physical and Mathematical Sciences. We are seeking a Research Fellow to contribute to the research agenda of the project ‘Geometric Optimization and Design in Learning and Robotics'. Key Responsibilities
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decision-making and research findings. Qualifications & Competencies: Minimally a PhD degree in Artificial Intelligence, Computer Science, Optimization, or a related field. Strong foundation in multi-agent