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Field
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-body ab-initio methods for description of electronic, magnetic, and vibrational properties in a range of materials Expertise with artificial intelligence and machine learning approaches will be also
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that combines mechanistic ecophysiology with AI, such as: Physics-informed machine learning and neutral networks to investigate plant physiological / abiotic relationships Bayesian statistics and neural and
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Previous Job Job Title NRRI-Post-Doc – Materials Electrochemistry Next Job Apply for Job Job ID 373733 Location Duluth Job Family Academic Full/Part Time Full-Time Regular/Temporary Regular Job Code
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. Basic Qualifications: A PhD in Materials Science & Engineering, Physics, Chemistry, or a related field completed within the last 5 years A minimum of 2 years of post-Ph.D. experience utilizing
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priorities. Willingness and ability to learn new research areas and contribute effectively with initiative and enthusiasm. Excellent written and verbal communication skills, with a demonstrated record of
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supervisor (Alternatively, provide names and e-mail addresses of 1-2 academic referees that potentially may be contacted). Personal data. You can read more about the requirements for your application here
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Posting Number R250137 Posting Link https://www.ubjobs.buffalo.edu/postings/58870 Employer Research Foundation Position Type RF Professional Job Type Full-Time Appointment Term Salary Grade E.89 Posting
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Posting Number R250137 Posting Link https://www.ubjobs.buffalo.edu/postings/58870 Employer Research Foundation Position Type RF Professional Job Type Full-Time Appointment Term Salary Grade E.89 Posting
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University of North Carolina Wilmington | Wilmington, North Carolina | United States | about 21 hours ago
Posting Details Posting Details Vacancy Number PD-0029 Working Title CSE Postdoctoral Scholar - (Pooled) Position Number PD0033 Location of Workplace Main UNCW Campus Brief Summary of Work for this
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in multiscale and multifidelity simulation techniques (ab initio methods at different fidelity, machine learning tight-binding, machine learning force fields, phase-field modeling, and/or kinetic monte