35 software-defined-network-phd Postdoctoral research jobs at Pennsylvania State University
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project management. This position requires a PhD in nematology (preferred), forestry, plant pathology, microbiology, microbial ecology, or a related field. Competitive candidates must have significant
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satisfactory performance. Degree/Field of Study: PhD in fluid dynamics, geomechanics, computational geomechanics by start date with Penn State. Applications must be submitted electronically and include a CV
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and build computational interventional models for individuals. Required qualifications: MD or PhD (completed by start of employment) in computer science or behavioral science. A technical background is
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mentoring of graduate students. The required qualifications are: PhD degree in Civil Engineering, Materials Science, Chemical Engineering, or related field Extensive research experience related to concrete
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, self-driven, collaborative, skilled young microscopist with a PhD in materials science, physics, chemistry, or a related field that has a thorough background in aberration-corrected scanning/transmission
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students. The required qualifications are: PhD degree in mathematics, science, engineering, or a related field by the start date. Extensive experience in one or more of the following areas: probabilistic
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presentation at conferences and manuscript preparation Specific criteria include: Recent MD or PhD (0 - 3 years) in a relevant biological science or bioengineering discipline Strong critical thinking skills
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cytometry General lab management and accurate documentation and maintenance of data files Animal handling and SQ, IP injection, oral gavage, intravenous injection Required Qualifications: PhD – biochemistry
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will have PhD in Biological, Chemical, Agricultural, Biosystems, or Environmental Engineering; or other related disciplines. The successful candidate must have completed all PhD degree requirements by
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research involving biological data analysis and modeling of biological systems. In particular, they will develop and apply algorithms to construct discrete dynamic models of signal transduction networks