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recovery that support real-time high-speed implementations. use machine learning to improve discrete modulation formats. develop MATLAB or python code. experimentally demonstrate the developed algorithms and
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. The activities are linked to other ongoing projects in the Section working on clean energy materials and machine learning for accelerated materials discovery. Qualifications Candidates should hold a PhD or
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modelling in physical systems and life sciences. Focus on advanced techniques and methodological advancements with real-world impact. Requires PhD in machine learning, experience in deep generative models
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formation and ionic transfer through the SEI. Candidates interested in machine learning methods could also pursue the development of equivariant graph neural network for electronic scale simulation of battery
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machine learning methods could also pursue the development of equivariant graph neural network for electronic scale simulation of battery materials and large-scale deployment of them in Europe's largest CPU
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such as Design Research, Interaction Design, Co-Design, Human-Computer Interaction or Computer Supported Cooperative Work as documented by a PhD dissertation and/or research publications experience in
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, especially on pricing problems, have experience with decomposition frameworks such as Danzig-Wolfe and Benders decomposition, have experience in the use of machine learning and reinforcement learning, have