27 computational-physics-"https:"-"https:"-"https:"-"https:"-"NOVA.id" positions at Stanford University
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Stanford University is one of the world's premier academic and research institutions, devoting tremendous intellectual and physical resources toward the betterment of humanity. As a major Silicon Valley
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. CERTIFICATIONS & LICENSES: When driving, must have a valid California Non-Commercial Class C license. PHYSICAL REQUIREMENTS*: Frequently perform desk based computer tasks, use a telephone, sit and use light/fine
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both spoken and written English. * Excellent organization skills. * Proficiency with Microsoft Office applications. CERTIFICATIONS & LICENSES: * None PHYSICAL REQUIREMENTS*: * Frequently use a computer
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, candidates should possess a strong background in quantitative fields such as Mathematics, Physics, Engineering, or Computer Science. This is a collaborative, cross-functional team, and project assignments will
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inventory of all duties, tasks, and responsibilities. Specific duties and responsibilities may vary depending on department or program needs without changing the general nature and scope of the job or level
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intellectual and physical resources toward the betterment of humanity. As a major Bay Area employer, Stanford seeks people committed to excellence and to improving our world. In turn, the university is committed
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, and Climate Energy Ventures). We are also currently exploring new initiatives and an alumni energy engagement program, with other new programs in the works. A new focus is to establish a team of
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interest in translational science. The postdoctoral fellow will work closely with Dr. Vivek Charu and Dr. Brooke Howitt. Required Qualifications: PhD in Biostatistics, Bioinformatics, Computational Biology
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the scope and impact of our studies. Current research themes include: The impact of GLP-1 receptor agonists on metabolic outcomes and surgical decision-making in bariatric patients Computer vision analysis
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research. The ideal fellow will be interested in developing and applying novel computational algorithms to novel datasets generated in the setting of non-neoplastic and neoplastic disease. Key