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SPECIFICS Postdoctoral Scholar Opportunity Sweetpotato Root System Architecture, Carbon Allocation & Stress Physiology Root and Tuber Crop Lab (RTC Lab) • Department of Plant Science • Penn State University
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. Qualifications: Extensive training in state-of-the-art economics and econometric analysis, including spatial statistics and other advanced quantitative methods. Familiarity with modern computational and data
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(development and validation of tools), pre-trial release, parole, and related topics. A Ph.D. in criminal justice, criminology, sociology, economics, public policy, or a related field is required at time of
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SPECIFICS The laboratory of Dr. Makova in the Department of Biology at The Pennsylvania State University seeks a Researcher, to work as part of a research team focused on the analysis of mutations in
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) Scholars who have completed the Ph.D. within the past two years A Ph.D. in criminal justice, criminology, sociology, economics, public policy, or a related field is required at time of appointment
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plant pathogens in organic production systems through a multi-institutional USDA/OREI funded project. The Postdoctoral Scholar will conduct or coordinate research computational analysis and pattern
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/biostatistics, computer science, bioinformatics, or other related disciplines is required. Strong interest, research background and experience in the methodology research in statistical genomics, machine/deep
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expected to collaborate with a faculty mentor and supervisor and graduate students. Applicants should preferably have research interests in applying computational methods (broadly defined) to one or more of
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experimental and/or data skills are welcome to apply. Experience in computer programming (e.g., Matlab, Python) and neuroinformatics tools is a plus but not required. Previous work with rats and basic surgical
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computational biology and bioinformatics, with experience in: Applying deep-learning or AI-based methods for image denoising. analysis of whole-genome sequencing (WGS) or other large-scale sequencing data