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of physics into machine learning and deep learning architectures to create accurate, physically consistent, efficient and interpretable/generalizable models. This PhD project will contribute to the development
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to modern genomic datasets that may involve hundreds of populations. He/she will also develop probabilistic GO models inspired from the Redundancy Analysis approach and extend it by introducing Neural
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, regression models, multistate models, simulation models, life table and decomposition approaches, causal inference, matrix population models). Desirable: B1. Scottish Credit and Qualification Framework level
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epidemiology and statistical and mathematical modelling For appointment at grade 7: A4 Normally Scottish Credit and Qualification Framework level 12 (PhD) plus track record of emerging independence within a
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models and observations. Your immediate leader will be the Head of Department. About the project The PhD candidate will join the Department of Biology and physically located at NTNU office in Trondheim
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, this interdisciplinary project will couple mathematical models of earthworm movement, stochastic models of the measurement process and designed experiments to improve earthworm detection. Project This project will work
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, or using climate modeling such as the Norwegian Earth System Model to constrain models and observations. Your immediate leader will be the Head of Department. About the project The PhD candidate will join
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The Groningen Institute for Evolutionary Life Sciences (GELIFES - https://www.rug.nl/research/gelifes/ ) offers a 4-year NWO M1-funded PhD position for a project on “Social Ageing: Social
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and self-motivated PhD candidate to contribute to our understanding of advances, gaps and opportunities for impact-based forecasting for vulnerable population groups across Africa. You will work at the
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Multiple PhD Scholarships available - Cutting-edge research at the frontiers of Whole Cell Modelling
Multiple PhD Scholarships available - Cutting-edge research at the frontiers of Whole Cell Modelling Job No.: 683222 Location: Clayton campus Employment Type: Full-time Duration: 3.5 to 4-year fixed