731 programming-"https:"-"https:"-"https:" Postdoctoral research jobs in United States
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data analytics using tools in programming languages such as Python, PyTorch, Pandas, Scikit Learn, etc., in applied problem-solving contexts. Understanding of machine learning algorithms (gradient
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the doctoral degree prior to the start date of the appointment. Demonstrated interest and a clear career development plan in implementation science. Strong writing, analytic, and communication skills; experience
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scheduled to receive, a Ph.D. in an area broadly relevant to our research program by the time employment starts. The individual should be highly self-motivated and is expected to lead their own individual
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Postdoctoral Research Associate I, Psychology (Genetically Informed Studies of Social Connectedness)
to U of A recreation and cultural activities; and more! The University of Arizona has been recognized for our innovative work-life programs. For more information about working at the University
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Post Doctoral Researcher for its Nanoelectronics and Microelectronics program. Candidates with a strong background in materials, semiconductor physics, and devices, especially with relevant material
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Strong econometric skills, as demonstrated by research using these methods Working knowledge of programming in Stata, SAS, or R, as demonstrated by application materials Contact Information to Applicants
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be an excellent team player, capable of working independently on multiple projects, and a clear academic career plan. Brief Description of Duties: The Postdoctoral Associate will work with Dr. Jiang
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and revision Educational Requirements: Work requires a PhD (bio)statistics, econometrics, data science, or related field. Experience/Skills Required: A background in commonly used programming
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using Python, MATLAB, or equivalent programming languages. Preferred Qualifications • Working knowledge of X-ray optics, detector systems, and beamline instrumentation. • Background knowledge in ultrafast
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experience in economic and supply chain analysis, computational modeling, or policy analysis. Proficiency in scientific programming languages (e.g., Python, R) and data analysis libraries (e.g., pandas, NumPy