138 algorithm-development-"Multiple"-"Simons-Foundation"-"Prof" positions at University of California, Berkeley
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their full potential. We actively support this by providing all of our full-time staff employees with at least 80 hours (10 days) of paid time per year to engage in professional development activities. Find
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recruitment allowances, and has numerous opportunities for faculty development and further training in mentorship. The posted UC salary scales set the minimum pay determined by rank and step at appointment. See
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their full potential. We actively support this by providing all of our full-time staff employees with at least 80 hours (10 days) of paid time per year to engage in professional development activities. Find
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to realize their full potential. We actively support this by providing all of our full-time staff employees with at least 80 hours (10 days) of paid time per year to engage in professional development
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by providing all of our full-time staff employees with at least 80 hours (10 days) of paid time per year to engage in professional development activities. Find out more about how you can grow your
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by providing all of our full-time staff employees with at least 80 hours (10 days) of paid time per year to engage in professional development activities. Find out more about how you can grow your
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for training in these techniques, and exceptional Specialists will over time take on responsibility for designing their own experiments, developing new ideas, presenting at lab meetings, and participating in
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to realize their full potential. We actively support this by providing all of our full-time staff employees with at least 80 hours (10 days) of paid time per year to engage in professional development
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will participate in quality assurance/performance measures, research development, and maintaining internal/external organizational liaisons. The incumbent is responsible for participating in a variety of
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, Associate, or Full Specialist. The primary research area will focus on applying advanced MRI methods and/or machine learning to evaluate patients with brain tumors. The candidate will be expected to develop