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analyses. Machine learning for biological data (e.g., protein language models, transformers, generative models) and interest in building interpretable tools for experimental colleagues. Qualifications PhD
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quantitative and machine learning approaches ● Developing predictive models linking nuclear features to future cell fate ● Interacting with collaborators in imaging, computational biology, and developmental
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processing, machine learning, and optimization theory. Strong verbal and written skills in English. Excellent analytical and problem-solving skills, and capacity to pursue independent research as
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worldwide and BNL is responsible for data compilation in North America. In order to make full use of the data in EXFOR, experience in data science, machine learning (ML) and applications of artificial
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neurological departments of Lyon's hospitals. Lyon is a vibrant and beautiful city, just 1 hour from the French Alps by car, and 2 hours from the Mediterranean coast and from Paris by train. More on our work can
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, integrate device engineering with clinical workflows, and apply artificial intelligence and machine learning for automated image and signal analysis, tissue classification, and real-time diagnostics
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using liquid biopsy next generation sequencing data for cancer diagnostics. About You Must have a strong background in next generation sequencing data analysis/machine learning, cancer and/or genome
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parameters. Applicants must hold a PhD degree or terminal degree from a recognized institution of higher learning with no more than five years post receipt of this degree, in Electrical/Electronics Engineering
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of optimization and machine learning. • Knowledge of reinforcement learning and black box optimization would be a plus. Skills • The candidate must be comfortable with algorithmic development using
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fellows will receive joint mentorship from leading experts in metabolic biology, AI and machine learning, drug delivery, and translational medicine, while maintaining full academic independence in research