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for VQAs using the underlying structure of molecules and their Hamiltonians – to minimize the number of quantum-classical computer cycles needed for convergence. - Learning functions to map molecules
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researchers working in AI across Sweden and abroad. Major responsibilities We are looking for a PhD student with an interest in neuro-symbolic AI (machine learning and symbolic methods) to work as a researcher
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PhD student with an interest in neuro-symbolic AI (machine learning and symbolic methods) to work as a researcher on this project, which will include both implementation work and experimentation. It is
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insight into the nuclear energy sector. You will find a field eager to learn of your findings, and also allowing you to develop new contacts and extend your scientific network. Project description
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experiments characterized by for example atom probe tomography or microscopy. It will give you unique insight into the nuclear energy sector. You will find a field eager to learn of your findings, and also
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motivated to work in the lab, learn new techniques and have the ability to take initiative. You should also be organized and able to work independently, as part of a collaborative research group. Good written
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characterization is a merit. The research project is experimental in nature and you should be motivated to work in the lab, learn new techniques and have the ability to take initiative. You should also be organized
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challenges by leveraging machine learning / artificial intelligence tools, which can not only provide mechanistic understanding but also enhance predictive capability of the green engineering processes. About
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at the division are organized in two research units: power grid and components and electrical machines and power electronics. In the research unit on electrical machines and power electronics, we are about 30
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fields such as software engineering (e.g., ICSE, FSE), formal methods (e.g., CAV, TACAS, FM, ATVA), robotics (e.g., ICRA, IROS), AI (e.g., AAAI, IJCAI, ECAI) or machine learning (e.g., ICML, ICLR