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., R, Python, SAS, or STATA) Experience working with large population dataset (e.g. EHR, claims data) Background in health informatics, population health research, or biostatistics, computer science
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. You will have demonstrable experience in the analysis of large-scale biological datasets, applying statistical modelling and computational approaches to high-dimensional data such as bulk and single
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team. · Applies statistical methods to large-scale, longitudinal training-related behavioral data. · Maintains substantial knowledge of state-of-the-art principles and theories
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We are seeking a Senior Researcher to join the Ideker Laboratory at the Big Data Institute (BDI) and the Nuffield Department of Population Health at the University of Oxford. The successful
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. Proficiency in SQL, Python/R, or similar tools; experience with big data platforms , machine learning, and data warehousing. Commitment to quality, integrity, confidentiality and compliance. Excellent
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. Certification and Licensing: Not applicable Experience: Experience in structural reliability analysis, probabilistic modeling, large language models, or computer vision is preferred Knowledge, Skills and
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data collection activities, ability and interest in working with large datasets, and interest in working with interdisciplinary teams of researchers. The expectation is that the scholar would help lead
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 13 days 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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in data integration, model design, and large-scale training by combining multi-modal scientific data, knowledge graphs, physics-aware machine learning, and GPU/HPC computing to develop transparent and
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biology and genomics. We are collaborative and multidisciplinary, combining organic chemistry, cell and molecular biology, protein biochemistry, and large-scale genetic screening in our research approach