194 programming-"the"-"DAAD"-"U"-"IMPRS-ML"-"IMPRS-ML"-"O.P" positions at Harvard University
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Noma-Reischauer Prize Student Organization Grant Other Graduate Opportunities Postgraduate RIJS Postdoctoral Fellowships in Japanese Studies Program on US-Japan Relations Postdoctoral Fellowships
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and Sciences (FAS) is the historic heart of Harvard University. It is the home of Harvard’s undergraduate program (Harvard College, founded in 1636) as well as all of Harvard’s Ph.D. programs
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Environment Forum programming. Fellows will receive stipends of $70,000, medical insurance, additional research support of $2,500, and (for those not already in residence in Greater Boston) $1,500 in moving
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existing Assets, and decommissions Assets when removed from service. Configures and maintains Preventive Maintenance programs in Maximo, in accordance with customer agreements. Designs reports utilizing
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to the breadth of resources and facilities available to members of Harvard University. For more information about the Kempner Institute Research Fellows program, please visit our website at www.harvard.edu/kempner
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. Successful candidates will be expected to contribute to technique development/material synthesis, plan and lead research projects, acquire and analyze experimental data, supervise and mentor undergraduate
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contribute to technical deliverables and help to plan for technology translation. Basic Qualifications PhD in engineering, biomechanics, or a related field. Additional Qualifications Interest in
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other universities in the region, and occasional visiting speakers. Fellows will also be encouraged to engage with the Center's Environment Forum programming. Fellows will receive stipends of $70,000
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at least two of the following: electrophysiology, rodent experimentation, programming of controllers, neuroprosthetics or neuromodulators, peripheral nerve surgery, neural interfacing models Track record
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to detail and organization skills Interest in learning and strengthening existing skillsets Strong technical and programming skills, familiarity with current deep learning techniques, with a focus on computer