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Field
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energy systems and demand response; process and energy system modeling; optimization and model predictive control; and applications of artificial intelligence and machine learning to energy systems
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English. More concretely your work package contains: We are seeking a full-time postdoctoral researcher to join an interdisciplinary research team working at the intersection of artificial intelligence
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to explore opportunities for and develop a medium-term plan for using artificial intelligence tools to support research administration, strategy, and development. The candidate will work with stakeholders
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for the challenges of today and tomorrow. These challenges are changing rapidly. That is why we focus on themes such as energy, climate, digitalisation, artificial intelligence (AI), and smart mobility every day. Our
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for the challenges of today and tomorrow. These challenges are changing rapidly. That is why we focus on themes such as energy, climate, digitalisation, artificial intelligence (AI), and smart mobility every day. Our
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of artificial intelligence and quantum science. Successful candidates will work closely with Prof. Di Luo and collaborators across Tsinghua University on cutting-edge topics at the intersection of AI, quantum
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provide the substrate necessary for artificial intelligence (AI) guided experimental design and optimization of scarce/costly resources, such as animal models. As AI capabilities advance, the computational
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(OVG) as part of the Correlates of Immunity – Artificial Intelligence (CoI-AI) Programme, a pioneering initiative that integrates controlled human infection models, advanced immunology, and AI-driven
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platforms a plus Experience applying machine learning, artificial intelligence, and large language models to research a plus The anticipated start date is September 1, 2026. The postdoctoral position incoming
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: Comparative genomics RNA secondary structure prediction Covariance models and RNA homology search methods Machine learning or artificial intelligence applied to biological data Transcriptomics analysis methods