64 computer-science-programming-languages-"University-of-Bristol" Postdoctoral positions at Duke University
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, Computational Biology, Computer Science, or a related field. Additional Qualifications: • Proficiency in Python, R, SQL, and/or other relevant programming languages; knowledge of Java or C++; familiarity with
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related to the economics of afforestation in low- and middle-income countries (LMICs). The planned research includes three components: (i) a systematic review on survival rates in tree-planting programs
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North Carolina, and it now has international programs in more than 150 countries. The Pratt School of Engineering at Duke University is one of the fastest-rising, highly ranked engineering schools in
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. The appointment is not part of a clinical training program, unless research training under the supervision of a senior mentor is the primary purpose of the appointment. The Postdoctoral Appointee functions under
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Duke University, Electrical and Computer Engineering Position ID: Duke -Electrical and Computer Engineering -POSTDOCYIRANCHEN [#30336] Position Title: Position Type: Postdoctoral Position Location
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Assessment Models (IAMs) such as GCAM or PAGE. The candidate must have a PhD degree in a related field, be fluent in computer programming, preferably python, and will ideally have experience in working with
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. • Collaborate with mathematical modelers and experimentalists in the NIH Center to iteratively refine learned models. Qualifications: • Ph.D. in applied mathematics, computational science, statistics, machine
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chemistry, geoscience, and/or green chemistry research is desired. The candidate should have the ability to and be excited to work in a diverse lab group setting, plan and conduct assessments independently
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-of-the-art IT infrastructure. Ideal candidates should hold a PhD in the area of Computer Science or Electrical and Computer Engineering and have strong programming skills including Python. Past work in
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, evolutionary biology, computer science, physics, applied mathematics, or engineering. Our research integrates mathematical modeling, machine learning, and quantitative experiments to understand and control