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Postdoc (f/m/d) Mechanical Characterization and Materials Assessment for Nuclear Applications / P...
data analysis and statistical methods # Interest in or initial experience with data-driven methods or machine learning is an advantage # Strong communication skills and a collaborative approach to
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economy. As a postdoctoral researcher, you will unravel how silicon suppresses liquid copper infiltration at the atomic scale, using density functional theory-accurate machine-learned potentials and
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Biology is desirable. Experience in the application and development of computational methods/tools or machine learning algorithms. Good computer programming skills in R/Matlab/PerlPython. Baylor College
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for employment has been made. Union Representation: This position is represented exclusively by Postdocs United/United Automobile, Aerospace and Agricultural Implement Workers of America (UAW) under the Collective
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framework for understanding emergent deception in human-AI interaction by uniting behavioural-psychological, economic-strategic, and machine learning perspectives. The postdoc will be jointly supervised by
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Area of research: Scientific / postdoctoral posts Job description: Postdoc (f/m/x) in Optoacoustic Tomography 102976 Full time 39 hrs./week Neuherberg near Munich At Helmholtz Munich, we develop
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University of North Carolina Wilmington | Wilmington, North Carolina | United States | about 11 hours ago
access helps us provide an enriching learning environment for students, faculty and staff interested in the marine sciences. College Center for Marine Science - 306 College College/School Information
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learning and deep learning. Specific Requirements PhD in acoustics. Knowledge of acoustical measurement techniques, as well as physical acoustics. Some experience in acoustic signal processing and machine
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boson decays, searches for supersymmetry and other new phenomena, and measurements of rare standard model processes. We vigorously pursue the use of machine learning techniques for data analysis
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, solvent-based recycling process for complex plastic waste streams such as multilayer packaging and e-waste, while Exergy will develop the digital-twin and machine-learning tools that make the process