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Field
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refractive-index imaging of complex samples. Apply machine learning and deep learning techniques to automate segmentation and quantitative analysis of tomographic refractive-index data from cells and tissue
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biology, advanced imaging, and machine learning. Colourbox via Unsplash Colourbox What skills are important in this role? The Faculty of Mathematics and Natural Sciences has a strategic ambition to be among
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intelligence/machine learning skills. The candidate’s research proposal must be closely connected to the call and the research of NCEI. Excellent skills in written and oral English. Personal suitability and
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the researchers from Department of Automation and Process Engineering will play a key role. We welcome motivated applicants in robotics, control, AI, machine learning, physics, and related fields, including early
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Science About the project This PhD project integrates pharmacoepidemiology, causal inference, and machine learning to study real-world treatment patterns, effectiveness, and safety of monoclonal antibodies
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the areas of stochastic analysis and computational methods towards machine learning with focus on risk-sensitive decision making and control. Techniques may include forward, backward stochastic differential
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severity of cyber threats. Both defenders and attackers are now using Machine Learning (ML) and Artificial Intelligence (AI), therefore research is needed to investigate and design more advanced
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of GIS, spatial statistics, or other spatially relevant methods. Demonstrated experience applying machine learning and AI-based approaches to empirical disease, ecological, or biological datasets, with
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groundwater/geochemical modelling software (e.g., MODFLOW, PHREEQC). Experience with laboratory analytical methods (e.g., chromatography, mass spectrometry). Familiarity with AI or machine learning applications
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(PDE). Examples of models in the scope of the project include particle models, stochastic PDE and models from fluid dynamics and machine learning. Place of work is the Department of Mathematics, Blindern