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genealogical relationships and genetic divergence across species, but its complexity requires new methodologies for efficient analysis. This project aims to use Variational Inference (VI) methods, enhanced by AI
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communicate strategic decisions. Preferred Qualifications (Meritable and Highly Valued): • Experience in leadership roles within complex, knowledge-intensive organisations. • Experience in managing staff
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at developing AI-driven solutions for complex sustainability challenges. To strengthen this area, we are now seeking a highly motivated and up-and-coming researcher for the position of Assistant Professor in
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northern Sweden create enormous opportunities and complex challenges. For Umeå University, conducting research about – and in the middle of – a society in transition is key. We also take pride in delivering
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independently. The workplace The Division of Automatic Control is successfully conducting research as well as postgraduate and undergraduate education within areas such as autonomous systems, complex networks
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(Metabarcoding) Write manuscripts for peer review scientific journal publications. Collaboration opportunities and research network The postdoctoral researcher will be part of an international research group
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communication skills. Other experience relevant to the third-cycle studies, e.g. professional experience. Other assessment criteria: Ability to manage and complete complex projects independently and efficiently
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supports forming a strong multi-disciplinary and international professional network between PhD-students, researchers and industry. Read more: https://wasp-sweden.org/graduate-school/ . Your work assignments
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Valued): Experience in leadership roles within complex, knowledge-intensive organisations. Experience in managing staff, budgets, and operations. Access to national and international networks within
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program (Data-Driven Life Science) with focus on precision medicine. Access to top-level infrastructure, a new therapy development initiative for brain diseases (CNSx3), and a strong network spanning