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generation of health data scientists. Areas of expertise include bioinformatics, computational biology, artificial intelligence, network science, Bayesian methods, spatiotemporal methods, visualization
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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | 2 months ago
the structure from such data is challenging, and new theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine
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and implement Bayesian graph neural networks and convolutional neural networks as surrogates for high-fidelity biomechanical models Quantify and propagate uncertainty, and develop strategies for model
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theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine learning. It also offers the opportunity to work with
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. quantitative and/or qualitative counterfactual-based approaches, Difference in Difference models, Qualitative Comparative Analysis, Bayesian hierarchical modelling); o Experience working with and synthesizing
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single cells make decisions during differentiation, in particular during development. Building on Bonsai, a Bayesian framework that leverages tree structures for distortion-free exploratory analysis
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model classifiers (PLS-DA, random forest, neural network, etc) towards unraveling materials structure-function relationships, and are familiar with optimization approaches such as genetic search, Bayesian
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environments and/or containerization Strong interest or familiarity with transposable element biology Ability to understand Expectation-Maximization algorithms or Bayesian statistical methods Track record
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to study chemical transformations in materials. 2. Artificial Intelligence Applications: - Leveraging conventional machine learning techniques for materials property prediction and Bayesian approaches
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Chekouo and his collaborators within and outside the University of Minnesota. The research will focus on the development of Bayesian statistical/machine learning methods for the data integration analysis