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to integrate heterogeneous molecular data, but are often less explicit about biological directionality and causal inference. This project instead builds on the structure of the central dogma, using genetic
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inference problems with Bayesian statistics, powerful MCMC methods have been proposed, for example the MCMC differential evolution and the Riemann Manifold Langevin Monte Carlo methods. Because
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mathematical background Core skills: Probability and statistics. Estimation, Bayesian inference, uncertainty quantification and calibration (proper scoring rules, reliability diagrams, ECE), experiment design
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European Molecular Biology Laboratory (EMBL) | Brandenburg an der Havel, Brandenburg | Germany | about 2 months ago
modelling, foundation models, cross-domain/-modality learning, explainable AI and mechanistic interpretability, representation learning, Bayesian inference, causal inference, active learning, AI-based agents
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Brandenburgische Technische Universität Cottbus | Cottbus, Brandenburg | Germany | about 2 months ago
gradient methods for high-dimensional neural networks, reinforcement learning, variational inference). In addition to methods development and theoretical research on modern AI and ML methods, the successful