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9th April 2026 Languages English Norsk Bokmål English English PhD Fellowship in Surrogate Modelling of Fluid Flows using Deep Learning Apply for this job See advertisement Job description The
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-source artificial intelligence, machine learning, statistical estimation methods, software tools, and big-data frameworks. Programming languages such as e.g. Python, C++, and LABVIEW. In the assessment
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that specifies the competencies that the Research Fellow will acquire. Access to career guidance will be provided throughout the doctoral education. Research topic This PhD project will investigate the safety and
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for Catalysis and Organic Chemistry at the Department of Chemistry. The group has extensive experience in computational modelling, reaction mechanisms, and machine learning for catalyst design and discovery. Nova
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qualifications: Experience with implementation or applications of large machine learning models Experience with generative methods for protein design and/or docking simulations or generative methods
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, and how their combination can improve safety signal detection. As a PhD fellow, you will be working with large-scale longitudinal data, managing data, writing scripts, performing statistical analyses
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results in foundational neural models, where models learn from large unlabelled image datasets, but also on additional data like clinical reports or electronic health rec-ords. The work will be done in
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application development. Deep Learning techniques, Data Engineering, and Semantic Technologies Open-source artificial intelligence, machine learning, statistical estimation methods, software tools, and big-data
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large language models, machine learning, and/or programming in R or equivalent programs is an advantage but not a requirement. Alongside developing their own research ideas, applicants should be capable
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exam before 15.06.2026. It is a condition of employment that the master's degree has been awarded. Background in optimization is required. Experience in machine learning is an advantage. Familiarity with