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Field
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specified by the Principal Investigator. Postdoctoral position in computational modeling of anxious avoidance at the Computational Mechanisms of Psychopathology Lab at Emory University (PI: Vanessa Brown, PhD
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at technical conferences. Position Requirements Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years) in mechanical engineering, materials science, civil engineering, computer
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sensors is important Qualifications: PhD in Mechanical Engineering, Chemistry, Bioengineering or related field Experience designing and validating diagnostic or analytical assays, including protocol
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computer science programs (Chemical Engineering, Civil and Environmental Engineering, Computer Science, Electrical and Computer Engineering, and Mechanical and Industrial Engineering). This two-year
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engineering-based research applicable to cardiovascular disease as part of an extramurally funded project. The successful applicant will create computational models from medical imaging data and run simulations
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Statement Qualifications Education & Experience PhD in materials Science, Mechanical Engineering and experience in Metal AM. Strong record of publication in additive manufacturing Knowledge & Skills N/A
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experience in scholarly research and a strong commitment to excellence in education are encouraged to apply. A PhD in Materials Science, Optics, Physics, Chemistry, Electrical, Chemical, Mechanical, Civil
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Experience with designing, packaging and testing neural implants Expertise in designing instrumentation for electrical stimulation of cells such as nerve cells Qualifications: PhD in Electrical and Computer
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) community-engaged research methodologies; (6) program or policy evaluation; and/or (7) implementation and dissemination. Ample opportunities exist to develop an independent program of research in one or more
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neuroscience. Qualifications Basic qualifications (required at time of application) PhD (or equivalent international degree) or enrolled in a PhD (or equivalent international degree) program. Additional