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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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of protein-based materials and biocatalysts using synthetic cells. A key objective is the bottom-up construction of synthetic cells that are capable of sustained protein production. This is a formidable
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statistical reasoning, including a clear understanding of model assumptions, uncertainty, validation, data structure, and the limitations of different analytical approaches. Experience with multimodal human
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description The Sars-Covid virus genomic RNA encodes a small number of structural proteins, one of which is the N-protein. This N-protein plays a strong role in packaging the genomic RNA; yet, how this
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How is the Sars-Covid genomic RNA packaged? Job description The Sars-Covid virus genomic RNA encodes a small number of structural proteins, one of which is the N-protein. This N-protein plays a
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integrating literature, in-house, and newly generated experimental data Build surrogate and predictive models that connect composition, molecular structure, synthesis and processing conditions, morphology, and
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, including active learning or Bayesian optimization. Experience with imaging, time-series or high-dimensional data. Exposure to crystallography or structural biology. Experience with multimodal datasets and
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on the theory of deep learning will investigate questions such as the structure and expressivity of emerging neural architectures relevant to space, such as implicit neural fields, continuous normalising
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constituent-specific remodeling laws that describe chronic changes in myocardial structure and function. You will couple tissue-level cardiac mechanics to systemic hemodynamic and neurohumoral inputs within
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carbon, nitrogen, and water flows in agroecosystems. A solid background in uncertainty quantification, applied statistics, Bayesian calibration, and Monte Carlo simulations. Strong skills in scientific