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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | about 1 month ago
data analysis methods to study biological memory circuits and their applications to machine learning. Building on recent work from the Fiete Lab, the role focuses on identifying principles of biological
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learning to improve detection using multiphase CT imaging and longitudinal biomarker data in routine practice. You will work with multidisciplinary clinical and AI teams on applied research, developing and
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implementation of novel training strategies under experimental constraints, e.g., active learning and other data-efficient approaches Conduct large-scale benchmarking and comparative evaluation of gene
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We are seeking a Senior Researcher to join a joint research programme between the Big Data Institute (BDI) in the Nuffield Department of Population Health and the Centre for Human Genetics in
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reconstruction and tracking performance evaluation Knowledge and programming experience in scientific Machine Learning Working knowledge of large-scale data processing Programming experience in C++, ROOT, and
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developing next-generation AI methods for healthy climate adaptation. The position will focus on building and evaluating foundation models for large-scale spatiotemporal health and environmental data. Our team
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background in statistics, and familiarity with large data sets such as metabolomics or epigenetic sequencing. Experience in epigenetics or gene regulation is a plus. Experience with statistical analysis tools
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this postdoc position, the focus is on methods and algorithms for large-scale graph analytics, in particular network science approaches for analyzing longitudinal, population-scale relational data derived from
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interpreting large datasets using statistical programming languages (e.g. R, Python etc.) Proven independent working style, technical problem solving, data analysis and generation of novel ideas Proficient
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scientific discovery with a focus on microscopy image analysis. Specifically, we are interested in the development of new methods to identify structures of interest in large datasets (detection, segmentation