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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 2 hours ago
combination of observational data, machine learning techniques, and cosmological simulations. The group is actively involved in multiple JWST Guaranteed Time Observation (GTO) and General Observer (GO) programs
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for cell activity, flow cytometry, cell sorting (e.g. BONCAT-FACS). • Quantitative skills in analysis of large datasets, for example: to integrate biological/genomic data with biogeochemical measurements
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hire’s education and experience. Special Instructions: A cover letter, resume, and contact information for three professional references (to include at least one previous supervisor) are strongly
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Leinweber Institute for Theoretical Physics (LITP). Potential research topics include: inflationary cosmology and its connection to late-time observables; large-scale structure theory and analysis; cosmic
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prospective screening trial at various screening centers of the Dutch Breast Cancer Screening Program, including analysis of large data sets of screening interpretation outcomes and of survey results, as
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satisfactory performance and funding availability. Applicants should possess a PhD degree in epidemiology, biostatistics, applied mathematics, data science or other related disciplines. They should have strong
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proficiently and timely, and analyzing data professionally. The scientists are expected to handle multiple projects, to write scientific papers and reviews, and to present work in scientific conferences
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, conducting, analyzing, and compiling data from a large number of AML cases. Qualifications Required: PhD in Clinical Genetics or equivalent Documented experience in multimodal single-cell analysis of leukemia
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individual who will be responsible for recruitment and data collection in the final work package of the ICALD programme: a randomised trial with stepped wedge design evaluating the delivery and implementation
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this project, we will develop neural diffusion techniques to design materials with targeted optical properties, scaling to large systems through efficient representations and GPU parallelization. We will also