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lit. b (postdoc) Limited contract until: Job ID: 6036 Explore and teach at the University of Vienna, where over 7,500 brilliant minds have found a unique balance of freedom and support. Join us if
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generating fusion energy. This research will focus on the chemical speciation and transport of tritium in the molten salt blankets using ab initio quantum simulations, machine learning potentials, and
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strategies within it. You will combine archaeological and landscape data (maps, satellite imagery, archaeological datasets) with machine learning approaches to build a system that highlights promising
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of electrochemistry and artificial intelligence. Ideal candidates will have experience in machine learning, large language models, AI-agent development and computational workflows, with particular interest in building
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, and machine learning within the scope of ACAG. The postdoc will have various opportunities for professional development, including contributing to and leading grant proposal development (e.g., external
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knowledge-grounded reasoning with flexible machine learning Tools that reduce manual burden while preserving traceability and clinical interpretability This position offers the opportunity to publish novel
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. The postdoctoral researcher(s) will join an international research environment at Umeå University, including Stat4Reg (www.stat4reg.se ), which develops statistical and machine-learning methods for register data
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, the Peabody Museum, or more technical parts of the University for periods of time to learn about both research and operational workflows. Connections with the Wu Tsai Institute, the AI at Yale program, the Data
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machine learning approaches to build a system that highlights promising locations for archaeological research. You are also expected to play an active role in project coordination and in writing follow-up
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/or oceanography; or experience working with numerical simulations, large data, scientific programming, and/or machine learning. Applicants are asked to send a CV, a brief statement of research