235 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"GUSTAVE-ROUSSY" positions at Harvard University
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observational field data, experimental data, and field experiments. Salary is $70,500 annually, plus benefits. Expected start date is mid-June 2027. For examples of our research, see our faculty pages: https
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observational field data, experimental data, and field experiments. Salary is $70,500 annually, plus benefits. Expected start date is mid-June 2027. For examples of our research, see our faculty pages: https
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Genomics at Harvard Medical School Several positions are available in the Park Lab ( https://compbio.hms.harvard.edu/ ). The aim of the laboratory is to develop and apply innovative computational methods
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mycobacterial molecular and phenotype data. Including code that is production-ready for dissemination to other laboratories and for diagnostic use, and the management of large-scale data. They will join a
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academic year. For history of the chair, see https://harvaus.fas.harvard.edu/ Incumbents of the Chair will ordinarily hold the title of Gough Whitlam and Malcolm Fraser Visiting Professor of Australian
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for a postdoctoral fellow position in the Laboratory of Systems Pharmacology ( LSP ; https://labsyspharm.org/ ), part of the Harvard Program in Therapeutic Science, at Harvard Medical School in
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and Machine Learning , with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable
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observational field data, experimental data, and field experiments. Salary is $70,500 annually, plus benefits. Expected start date is mid-June 2027. For examples of our research, see our faculty pages: https
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images contain rich information on complex diseases. The goal of our efforts is to build and apply automated analytical pipelines for various types of pathology data, including histopathology images and
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methods to compare the genetic architecture of rare vs. common genetic variation 2. Developing methods to analyze GWAS data using graphical models and genome-wide genealogies 3. Developing methods to