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conducting quantitative analyses or master game theoretic analysis. Experience with large language models, machine learning, and/or programming in R or equivalent programs is an advantage but not a requirement
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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 Europe’s leading communities
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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
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large datasets analyses Active participation in LALP Lab activities Required selection criteria You must have completed a doctoral degree in cognitive science or computer design/programming Training and
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language processing or computational linguistics; alternatively, in computer science or machine learning with a specialization in natural language processing Documented knowledge of core machine learning methods and
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methodologies Experience with machine learning techniques Experience with pipeline development and testing (gitlab, simulated light curves…) Ability to work independently and to collaborate in an international
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for evaluation by the closing date. Only applicants with an approved doctoral thesis and public defence are eligible for appointment Strong programming and artificial intelligence/machine learning skills Interest
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four years are expected to acquire basic pedagogical competency in the course of their fellowship period within the duty component of 25 %. Place of work is Department of Chemistry at Blindern/Gaustad
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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