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offer Joining our team will provide you with numerous benefits, including: The opportunity to explore completely new regimes of physics and information, both theoretically and experimentally
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on the theoretical foundation of machine learning. Your CV comprises: A strong relevant background within machine learning and mathematics. Extensive experience programming machine learning models. An active interest
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. The approach will be validated by application to industrial production at a partner company. The position includes both theoretical and experimental work and carries project as well as academic responsibilities
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production at a partner company. The position includes both theoretical and experimental work and carries project as well as academic responsibilities. The scientific work conducted must be positioned relative
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(ORR), oxygen evolution reaction (OER), and carbon dioxide (CO₂) reduction. Collaborating with theoretical research groups to guide the design of active site structures through computational modelling
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: Theoretical understanding of photonic integrated circuits. Additionally, we expect that you: are academically curious and think deeply and creatively. have a strong internal drive and take responsibility
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to publications and other relevant communication Qualifications Relevant university master's degree Good theoretical background in organic chemistry, molecular biology or biotechnology Experience with analytical
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. The approach will be validated by application to industrial production at a partner company. The position includes both theoretical and experimental work and carries project as well as academic responsibilities
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generally. The expectation is that the PhD project can extend on and further develop these theoretical debates by applying them to extreme wealth. The central question for WP2 is thus: What role should we
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environmental engineering. A collaborative and analytical mindset, with the ability to synthesize complex information across experimental and theoretical domains. Enthusiasm for advanced electron microscopy