39 molecular-modeling-or-molecular-dynamic-simulation Fellowship positions at UiT The Arctic University of Norway in Norway
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in the context of a UiT grant that focuses on modeling spatio-temporal medical image analysis with a particular focus on learning from limited labelled data. The successful candidate will be a part of
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Chemistry » Molecular chemistry Chemistry Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country Norway Application Deadline 25 Sep 2025 - 23:59 (Europe/Oslo) Type of Contract
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dynamics, sediment sources, carbon cycling, during past warm periods; provide geophysical constraints for i2B numerical modelling teams for example, palaeo-bathymetry, palaeo-ice dynamics and palaeo
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understanding, including changes in current patterns, glacial dynamics, sediment sources, carbon cycling, during past warm periods; provide geophysical constraints for i2B numerical modelling teams for example
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in the context of a UiT grant that focuses on modeling spatio-temporal medical image analysis with a particular focus on learning from limited labelled data. The successful candidate will be a part of
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optical and mechanical simulations, possibly also computational fluid dynamics. Fabrication of the PIC in cleanroom facilities in Norway (NTNU Nanolab and at UiT), including process optimization and
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life stages, molecular biological methods, and RAS will be regarded advantage. Knowledge on RAS related water chemistry, especially challenges related to ammonia exposure, and experience with lab work
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, and curated databases into deep models, enhancing accuracy, interpretability, and robustness. Probing and steering the internal mechanisms of deep models by aligning their representations with human
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optical and mechanical simulations, possibly also computational fluid dynamics. Fabrication of the PIC in cleanroom facilities in Norway (NTNU Nanolab and at UiT), including process optimization and
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, and curated databases into deep models, enhancing accuracy, interpretability, and robustness. Probing and steering the internal mechanisms of deep models by aligning their representations with human