22 phd-energy-or-power-or-grid-or-optimization Postdoctoral positions at Chalmers University of Technology
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We are looking for a Postdoc to become part of our team at the Division of Subatomic, High-Energy and Plasma Physics at the Department of Physics. Join our innovative team and contribute to exciting
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-14158 Is the Job related to staff position within a Research Infrastructure? No Offer Description We are looking for a Postdoc to become part of our team at the Division of Subatomic, High-Energy and
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-Energy and Plasma Physics at the Department of Physics. Join our innovative team and contribute to exciting research in theoretical fusion plasma physics in a collaborative and dynamic environment. About
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the Competence Centre for Catalysis (KCK) and the Chalmers Area of Advance Energy, which provide strong networks and resources for your research. Project overview The project aims to explore new catalytic
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research, often in collaboration with industrial partners. The project is part of the Competence Centre for Catalysis (KCK) and the Chalmers Area of Advance Energy, which provide strong networks and
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to the application deadline*. Proven ability to work across disciplines Research experience in electrochemical energy storage, battery materials, or multifunctional composites Experience with characterization
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systems demand power amplifiers combining high efficiency, linearity, and frequency agility. Meeting these requirements calls for a deeper understanding of how semiconductor device properties influence
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Machine Learning Integration Develop and implement machine learning algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC
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, the work might involve implementing new algorithms in the SCT tool Supremica, which is developed by the Automation group. Main responsibilities Conduct research in collaboration with senior researchers and
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algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC) to accelerate design iterations Integrate ML approaches with finite