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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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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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to machine learning algorithms in order to get uncertainty estimates for parameters governing the distribution of the observed data. The predictive Bayes scheme for uncertainty quantification contains a wide
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and postdoctoral fellows, including individually tailored career development plans with formal supervision and project-based learning. Secondments, consortium meetings, and workshops will provide hands
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design and in silico validation intimately connected to experimental validation. In this project, you will develop machine learning methods and apply them in an interdisciplinary environment spanning
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. Qualification requirements A PhD degree within neuroscience, psychology, medicine, machine learning or biology or equivalent. Doctoral dissertation must be submitted for evaluation by the closing date
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environment for the training and development of PhD candidates and postdoctoral fellows, including individually tailored career development plans with formal supervision and project-based learning. Secondments
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environment spanning physics, developmental biology, advanced imaging, and machine learning. Colourbox via Unsplash Colourbox What skills are important in this role? The Faculty of Mathematics and Natural
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), enabling cross-contextual learning and refinement of policy recommendations. A postdoc with expertise in urban built environment studies and qualitative social sciences will play an important role in