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UiO/Anders Lien 9th August 2026 Languages English English English Join Integreat in Norway! Eight PhD fellowships in machine learning await. Collaborate, innovate, and thrive! PhD Fellowships in
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of Philosophy (PhD) in biomedical informatics, computing, computer science, data science, artificial intelligence, or a closely related field. PhD completed within the last 10 years preferred. AI/ML Expertise
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programme. Requirements: Education: PhD in chemistry, chemical engineering, materials science, biochemistry or equivalent. Knowledge: Extensive expert use of an array of physicochemical and materials
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requirements, what we are really looking for is a creative, quantitatively strong scientist who likes to invent. Your PhD might be in physics, computer science, computational or systems biology, bioinformatics
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system to track proposals. Evaluate and perform preliminary analysis of the data using graphs, charts or tables to highlight the key points of the research results collected in accordance with the research
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modeling and statistical analyses in collaboration with project principal investigator and team’s PhD-level biostatistician, including descriptive and multivariable statistical analyses. Performs analyses
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. Experience applying machine learning to networking problems, for example, reinforcement learning, graph neural networks, or uncertainty quantification. A track record of publications in leading networking
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Foundation Health Model. As a PhD candidate, you will conduct deep-dive research into training pipelines and reasoning techniques for clinical foundation models. You will join an elite, interdisciplinary team
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PhD Studentship: Bottom-up Decoding of Protein Conformational Landscapes: from Gas-phase to Solution
misfolding-related diseases such as Alzheimer's - but predicting how proteins fold in biological environments remains a key unmet challenge. This project brings together insights from efficient graph-driven
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. Experience applying machine learning to networking problems, for example, reinforcement learning, graph neural networks, or uncertainty quantification. A track record of publications in leading networking