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capillary pressure, and data-driven, physics-driven machine-learning. Applications are sought for a two-year postdoc position, and will work closely with an industry partner. The position will include a
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Application documents: 1) Brief cover letter, explaining your motivation for applying, 2) Detailed curriculum vitae (including your email address), 3) Complete transcript of grades from all your university-level studies. We do not ask for more information/documents at this point (but you can...
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the Climate and Livability Initiative (CLI). The successful candidate will engage in machine learning (ML) and artificial intelligence (AI) projects aimed at forecasting natural hazards, assessing climate
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Postdoctoral Researchers to work on research problems at the interface between a selection of the following areas: - Bayesian inference for deep learning - Generative modeling - Computational statistics
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understanding of Bayesian principles for Statistics and Machine Learning High proficiency in programming with languages, such as: C/C , Python (including pytorch/jax) A proven record of the ability of producing
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learning, deep learning, and AI) for analysis and prediction of genotypic variation Methodology (machine learning, deep learning, and AI) for analysis and prediction based on medical or biological imaging
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Bioinformatics (generative protein design) Methodology (machine learning, deep learning, and AI) for analysis and prediction of genotypic variation Methodology (machine learning, deep learning, and AI
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specific properties with AI or machine learning Cell-free expression systems: utilizing isolated cell components for metabolic reactions Synthetic gene circuits: programming genetic circuits for complex
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biosynthesis Biomolecular design: engineering proteins, enzymes, and other biomolecules with specific properties with AI or machine learning Cell-free expression systems: utilizing isolated cell components
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, analytical and reactor microfluidics Organs on a chip for diagnostics and drug discovery Methodology (machine learning, deep learning, and AI) for the analysis, biomarker discovery, and predictive models based