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applicants for a postdoctoral fellow position in the Kruse lab at Harvard Medical School in Boston, Massachusetts. The work of the Kruse lab is focused on membrane protein signaling, with a particular focus on
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to the appointment. The fellowships provide an annual stipend of $70,000. Fellows are expected to engage with the Canada Program and with the University’s wider community. Fellows receive shared office space at
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Position Description The successful candidate will work on using satellite observations of atmospheric methane to better quantify methane emissions on regional to global scales through inverse analyses
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experience, will work as part of a larger team to assist with collecting and analyzing data gathered from human subjects, both in field, clinic and lab studies as part of evaluations of the technology. A large
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relevance to cognitive decline and resilience. Anchoring deep individualized phenotyping, the work will involve a combination of brain imaging, biomarker assessment, and ambulatory behavioral assessments
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available in the Geometric Machine Learning Group at Harvard University, led by Prof. Melanie Weber. This role offers an opportunity to perform research on Riemannian Optimization. The ideal candidate has a
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Postdoctoral Fellow position. The Lab uses biotechnologies to discover and design new approaches to cardiovascular and metabolic diseases. We work at this interface using a broad variety of techniques in
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will involve investigating the physiological and functional variation of plants using spectroscopy and will include greenhouse work, field work, plant phenotyping, computational analyses of hyperspectral
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for a postdoctoral fellow position in the Mental Health for All Lab (MHFAL) at the Department of Global Health and Social Medicine at Harvard Medical School in Boston, Massachusetts. The work of the MHFAL
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and statistical genetics. Potential research projects include (but are not limited to) developing statistical methods and theory for large-scale multiple testing, variable selection, spectral clustering