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to the development and use of new in vitro model systems for neurodegenerative diseases and brain cancer. The group is led by Henrik Ahlenius and includes 3 postdocs, an associate researcher, and a number of master’s
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application! We are looking for a PhD student for sustainable and resource-efficient machine learning. Your work assignments Machine learning has recently advanced through scaling model sizes, training budgets
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equations. Your main research assignments will be to develop new models and methods for generative sampling and Bayesian inference. You will be jointly supervised by Assistant Prof. Zheng Zhao (https
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well as physically-based hydrological model development. The principal supervisor will be Ylva Sjöberg at Umeå University, and the research involves an interdisciplinary team of collaborators at Gothenburg University
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scaling model sizes, training budgets, and datasets; often at substantial computational and environmental costs. This PhD project targets sustainable and resource-efficient machine learning with a focus on
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model for large scale assessments and projections of the land-water carbon cycle to variation in climate conditions. The detailed direction of the PhD studies will be discussed and decided jointly with
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involved in several of these cutting-edge research projects and in the societal transformation that they entail. We offer a broad range of courses and study programmes to match the skills in demand. We hope
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fever or pulmonary syndrome), overcome the glycocalyx barrier to reach the cell surface. Employing cell models with a thick glycocalyx as well as synthetic molecularly-defined glycocalyx mimics, you will
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utilizing Python and web-stack technologies (such as JavaScript), to translate theoretical models into functional, testable software in close collaboration with our core research team. Beyond software
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and CH4) from headwaters, and use of machine learning and process-based model for large scale assessments and projections of the land-water carbon cycle to variation in climate conditions. The detailed