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Field
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 7 hours ago
-scale datasets, ideally including remote sensing or weather/climate data; strong programming skills in Python and experience with ML frameworks; and familiarity with uncertainty quantification methods. 4
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systems (Mojo, Julia, Rust, Python), and HPC system co‑design. This position is embedded within the larger DOE ASCR ecosystem, with direct relevance to ongoing efforts, and related AI‑for‑HPC thrusts
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of genome biology, bioinformatics and/or machine learning. Experience with python/R programing, machine learning theory and tools. Preferred Qualifications Publication record with minimum 2 years experiences
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programming and data analysis using R, Python, MATLAB, or comparable platforms. Skill in oral and written scientific communication, as demonstrated through peer‑reviewed publications and conference
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Sub-Saharan Africa and conducting epidemiological research on pediatric cancer disparities for patients treated in the US. The Postdoctoral Associate will use R or Python to maintain data pipelines
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to therapeutic discovery You bring PhD in computational biology, bioinformatics, computer science, or related field Strong coding skills (Python required; ML frameworks preferred) First-author publication(s
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research will involve Python scripting within the Thermo Fisher AutoScript environment to control operation of the microscope beam, stage, detectors, and spectrometers, with a particular emphasis on annular
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biology, or a related field Experience designing, executing, analyzing, and interpreting quantitative experiments, including high-throughput screens. Experience with coding software such as Python, R, and
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physiology Competency in scientific programming (e.g. ImageJ, Python) for data analysis and image processing. About the Lab & Environment. The Krishnakumar lab is a highly collaborative group with access
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to and build upon modern Python-based statistical modeling tools such as pyhf, supporting likelihood publication, large-scale analysis combinations, and result reinterpretation across the ATLAS