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in writing grant applications and working with machine learning approaches such as MaxEnt, random forest, neural network. Experience using Geographical Information Systems and ecological niche modeling
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model for innovative medical education and a centre for transformative research. The School’s primary clinical partner is the National Healthcare Group, a leader in public healthcare recognised
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technology. The planned work is experimental and will be conducted in our lab facilities, also incorporating theoretical models of complex flow. Fieldwork is planned in collaboration with a non-profit
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Architecture, Marine Engineering, Mechanical Engineering or related field. Proficiency in hydrodynamic modeling tools (e.g., ANSYS/Star CCM/Fluent/Open Foam etc.) and finite element analysis software (e.g
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microscopy. Experience with cancer organoid models and/or bioinformatics is an advantage. We offer broad training possibilities in the required experimental methods within a stimulating academic environment in
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models, collaborative consumption models, infrastructure needs, social enterprises, financial incentives and systems that could be developed to support and scale-up the transition to the circular economy
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Integreat develop theories, methods, models, and algorithms that integrate general and domain-specific knowledge with data. By combining the mathematical and computational cultures, and the methodologies
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at Integreat - Norwegian Centre for Knowledge-driven Machine Learning is a Centre of Excellence, funded by the Research Council of Norway. Researchers at Integreat develop theories, methods, models, and
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model for innovative medical education and a centre for transformative research. The School’s primary clinical partner is the National Healthcare Group, a leader in public healthcare recognised
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fields. Foreign completed degree (M.Sc.-level) corresponding to a minimum of four years in the Norwegian educational system Fluent oral and written communication skills in English. Desired qualifications