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Field
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physics, bioengineering, electrical or mechanical engineering, computer science, or a related field with experience building imaging instrumentation and interest in biological applications. Hands-on work
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Requirements • PhD in bioengineering, biochemistry, biology, human physiology, biophysics, physics, chemistry, chemical engineering or related disciplines • One year of lab experience Professional Competencies
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presentations). Evidence of their contributions to their current research communities. Track record of mentoring more junior scholars. Required Qualifications: PhD in computer science, electrical engineering
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Electrical Engineering, Computer Science, Operations Research, or a closely related field. Experience in power systems, distribution systems, or microgrid modeling, with a solid understanding of advanced co
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, Medical Laboratory Science, and Forensic Science, and Masters and PhDs in Biology, Biochemistry and Molecular Biology, and Environmental Science. We measure our success by the success of our students and
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normal and pathological feeding and reward processes. Applicant must hold a PhD or equivalent degree in neuroscience, biochemistry, molecular biology, psychology, computer science, biomedical engineering
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Engineering, and an interest in studying the earliest stages of Alzheimer’s disease. The Postdoc will participate in lab meetings, research-in-progress seminars, and presentations at conferences. They will
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engineering, computer science, and any related fields, or MD with the equivalent level of training and lab experience. • PhD must be awarded no more than four years prior to the effective date of appointment
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to excellence in education are encouraged to apply. A PhD in Materials Science, Optics, Physics, Chemistry, Electrical, Chemical, Mechanical, Civil or Bio Engineering or related area is required. We
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computational modeling of behavior to identify the underlying circuit computations. Current projects in our laboratory emphasize task-structured behavior assays, including set shifting, reversal learning