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of metabolic and cellular properties Phylogenomic analyses of obtained MAGs, including extraction and evaluation of marker genes, performing ML and Bayesian analyses of (concatenated) marker gene sets using
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the next. Your models will first be used to analyze completed experiments and identify trends, and later integrated into active learning and Bayesian optimization frameworks to suggest which experiments
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varying material properties. The resulting response will be analyzed using techniques such as Monte Carlo simulations. Identifying the variability of the model parameters using Bayesian inference
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of Mathematics at Radboud University (Nijmegen, Netherlands), and join the research group of Laura Scarabosio, funded by the NWO Vidi programme ’Taming Frequency in Bayesian Inverse Wave Scattering’. Inverse wave
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programming techniques (e.g., techniques for differentiating effectful programs such as gradient estimation of probabilistic programs, implicit function differentiation, compositional Bayesian inference
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) physics. Desirable Expertise in computational fluid mechanics, broadly construed. Expertise in Bayesian methodology for optimization and experiment design. Experience with equivariant neural networks. Track
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chemistry concepts (desirable). Familiarity with chemical or biological databases (e.g., ChEMBL, PubChem, PDB) is a plus. Experience with Bayesian modelling, transfer learning, few-shot learning, or other
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of the Department of Mathematics at Radboud University (Nijmegen, Netherlands), and join the research group of Laura Scarabosio, funded by the NWO Vidi programme ’Taming Frequency in Bayesian Inverse
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to improve R&D efficiency, and the influence of investors and other external actors on entrepreneurial outcomes. Our research also examines decision-making under uncertainty, including the use of Bayesian
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. Desirable Expertise in computational fluid mechanics, broadly construed. Expertise in Bayesian methodology for optimization and experiment design. Experience with equivariant neural networks. Track record