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Field
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Bayesian inference, probabilistic modeling, and machine learning, the project aims to make Arctic observations more efficient, intelligent, and impactful. You will integrate field observations—including
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project “Actively learning experimental de-signs in terrestrial climate science (ACTIVATE)”: https://www.mn.uio.no/geo/english/research/projects/activate/index.html The PhD fellow will be part of a growing
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is home to a consortium of postdoctoral fellows who provide modeling expertise for a wide range of projects as integral members of those research teams. Unit URL https://imci.uidaho.edu/ www.uidaho.edu
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page to watch video, or click here to open video) About the position The position is part of the research project “Prediction of genetic values and adaptive potential in the wild (GPWILD)” (https
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Elhoseiny, Code: https://github.com/yli1/CLCL Uncertainty-guided Continual Learning with Bayesian Neural Networks (ICLR’20), Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, Marcus Rohrbach, Code: https
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format. The application should be registered via Umeå University’s e-recruitment system Varbi (https://umustipendie.varbi.com/en/what:job/jobID:950146/ ) and submitted by the deadline 17th of September
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systems (e.g., VINCI). Due to federal requirements, eligibility may be limited based on citizenship or visa status. Experience using or modifying machine learning models such as decision trees, Bayesian
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is part of the ERC-funded project “Actively learning experimental de-signs in terrestrial climate science (ACTIVATE)”: https://www.mn.uio.no/geo/english/research/projects/activate/index.html The PhD
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in chemistry and biology, approaches for extracting relevant information from foundation models, and/or methods for adaptive experimental design such as active learning or Bayesian optimization