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technologies, as is proficiency in advanced statistical analysis- particularly in R or similar- for interpreting large environmental datasets. Ideal candidates will have a robust publication record, experience
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, Uncertainty quantification, Approximation Theory, Applied Probability and Bayesian statistics, Optimal Control and Dynamic Programming. Appointment, salary, and benefits. The appointment period is two years
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and Statistics (11 ) Physics (16 ) Science & Engineering-related (2 ) Social and Behavioral Sciences (28 ) Age: Must be 18 years of age Affirmation I certify at the time of application that I
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Sciences (14 ) Life Health and Medical Sciences (48 ) Mathematics and Statistics (11 ) Physics (16 ) Science & Engineering-related (2 ) Social and Behavioral Sciences (28 ) Age: Must be 18 years
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information, please visit our Benefits Page. CAMPUS SECURITY CRIME STATISTICS Pursuant to the Jeanne Clery Disclosure of Campus Security Policy and Campus Crime Statistics Act and the Pennsylvania Act of 1988
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Are you keen to contribute to accelerating sustainability transitions through regulatory innovations and policy experimentation and learning? Enjoying transdisciplinary action research? Job description The project ‘From EXperiment to sustainable change: TRAnsformative methodologies for...
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communication systems and their bottlenecks. Apply statistical and machine learning techniques to this data to gain insights. Project Collaboration: Work closely with other researchers and engineers
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National Energy Technology Laboratory (NETL) | Pittsburgh, Pennsylvania | United States | about 12 hours ago
Geosciences (21 ) Engineering (29 ) Environmental and Marine Sciences (14 ) Life Health and Medical Sciences (51 ) Mathematics and Statistics (11 ) Physics (16 ) Science & Engineering-related (2
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for predicting the reliability of high-temperature structural components. This involves working with various continuum damage mechanics models and statistical reliability models. The goal is to enhance engineering
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or willingness to develop skills in analytical techniques such as GC–MS or related methods for quantifying seed-applied compounds Experience with statistical modeling, experimental design, and multivariate