187 parallel-computing-numerical-methods-"Multiple" Fellowship positions at Harvard University
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Director of LISH (Dr. Ramona Pop). The position involves conducting rigorous empirical research using field experiments, large-scale data analysis, and computational methods to advance our understanding
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have access to multiple amenities including a roof top terrace with stunning views of Boston and proximity to numerous restaurants and cultural attractions. We value an inclusive and diverse workforce
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global seed-bank coverage of species under investigation. The Davis Lab of Plant Biodiversity explores questions in plant evolution and ecology using a variety of cutting-edge methods. Current and past
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for Biomedical Imaging (Harvard/MIT/Mass General). In parallel, there will be opportunities to analyze and publish existing data upon identifying areas of mutual interest. The appointment is for one year with a
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Details Title Harvard Quantum Initiative Prize Postdoctoral Fellow School Multiple Schools - Joint Search Department/Area Harvard Quantum Initiative Position Description The Harvard Quantum
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have access to multiple amenities including a roof top terrace with stunning views of Boston and proximity to numerous restaurants and cultural attractions. We value an inclusive and diverse workforce
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computer science, statistics, operations research, or related computational fields. As part of an interdisciplinary research team dedicated to advancing management science, the fellows will develop novel
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sophistication, including strong statistical skills and comfort with large-scale or complex data. Experience with computational text analysis, such as NLP methods, historical text processing, topic modeling
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methods into clinical applications to enhance oral health care. Under the supervision of principal investigators, the selected candidate will collaborate with a multidisciplinary research group, working
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collaborations among operations researchers, statisticians, and computer scientists to overcome the methodological challenges posed by the misalignment between historical methods underpinning modern data science