22 bayesian-structure-"https:" Postdoctoral positions at Oak Ridge National Laboratory
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descent, random forests, etc.) and deep neural network architectures (ResNet and Transformers). Preferred Qualifications: Knowledge of Approximate, Local, Rényi, Bayesian differential privacy, and other
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, finite volume, and machine learning to solve challenging real-world problems related to structural materials and advanced manufacturing processes. The successful candidate will have experience with
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characterizations. Experience with user facilities. Data analysis of structural, electronic, magnetic, and topological properties. Work with others to maintain a high level of scientific productivity. Publish
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, Wellness Programs, Educational Assistance, Relocation Assistance, and Employee Discounts. We are not accepting applications for this job through MathJobs.Org right now. Please apply at https://jobs.ornl.gov
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: ORNLRecruiting@ornl.gov We are not accepting applications for this job through MathJobs.Org right now. Please apply at https://jobs.ornl.gov/job/Oak-Ridge-Postdoctoral-Research-Associate-TN-37830/1331751500
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, Educational Assistance, Relocation Assistance, and Employee Discounts. We are not accepting applications for this job through MathJobs.Org right now. Please apply at https://jobs.ornl.gov/job/Oak-Ridge
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Employee Discounts. We are not accepting applications for this job through MathJobs.Org right now. Please apply at https://jobs.ornl.gov/job-invite/15422/ . Postal Mail: 1 Bethel Valley Road Oak Ridge, TN
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structure and statistical mechanics codes, and data science tools would be highly desirable Excellent written and oral communication skills. Motivated self-starter with the ability to work independently and
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Director's office can be found here: https://www.ornl.gov/content/research-integrity . Basic Qualifications: A PhD in physics, chemistry, biochemistry or a related field completed within the last five years
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Postdoctoral Research Associate - Theory-in-the-loop of Autonomous Experiments for Materials-by-Desi
of NTI and CNMS to develop HPC workflows that can perform multi-fidelity simulations to predict and interpret a wide range of structural and electronic characterization techniques Develop physics-informed