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to contribute to developing new ones Fluency and experience with high performance and/or high throughput computing Expertise in the theory and usage of high-level Bayesian and/or frequentist statistical data
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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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, internationally connected research programme spanning Bayesian infectious disease modelling, AI-driven epidemic forecasting, genomic epidemiology, and pandemic preparedness. The postholder will work with Asst. Prof
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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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agent based/individual based modelling, SEIR modelling, geospatial statistics, Bayesian statistics, burden mapping, measuring the impact of the environment on disease among others. The PI has projects in
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PK/PD studies, Bayesian model-based dose-finding approaches, adaptive designs and master protocols, including basket and umbrella trials. You will be expected to build productive collaborations across
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Experience with population genetics or statistical genetics Familiarity with Bayesian methods, probabilistic modeling, or graphical models Experience with scientific computing in Python, JAX, Torch, Julia, C
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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