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Field
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. Sparse sensing, optimization, or Bayesian experimental design. Machine learning for imaging. Synchrotron experiment experience. Semiconductor devices or metrology. We offer We offer a fully funded
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(or similar): Coherent diffractive imaging, especially ptychography. Sparse sensing, optimization, or Bayesian experimental design. Machine learning for imaging. Synchrotron experiment experience. Semiconductor
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motivated researcher to develop a strong independent research profile at the interface of Bayesian statistics, clinical trial design, optimization, computational statistics, and/or translational cancer
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statistical models (Bayesian approaches, geographical analyses) adapted to environmental data. • Carry out statistical analyses and the spatial distribution of risk between residential environmental exposures
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understanding of data quality, reproducibility and robust analytical practice. Experience of SQL, cloud-based or high-performance computing environments, and Bayesian methods would also be valuable. Beyond
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modeling, sensitivity and robustness analysis, Bayesian inference, inverse problems, parameter estimation, or model validation. Experience or strong interest in scientific AI/ML, including surrogate or multi
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, computational statistics, optimisation, Bayesian statistics.Specific requirements: Proficiency in Python and R. Additional Information Benefits Monthly Maintenance Allowance: €1,901.00 Funding Entity
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, ● Methods for heterogeneous treatment effects estimation, ● Methods for multiple exposures, multiple outcomes, ● ML and AI methods for causal inference, ● Bayesian causal inference, ● methods
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that leverage state-of-the-art AI methods (deep learning, generative AI, Bayesian modelling, active learning, etc.) to combine cellular imaging data, chemical compound structure, viral genomes and other omics
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via nonlinear parametrizations such as deep networks, dynamical systems and control, Bayesian inference and generative modeling, and randomized linear algebra. Applications of interest are transport