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process It is anticipated that the selection process will take place on 8th July. This will consist of an interview, test and tour. We plan to let candidates know if they have progressed to the selection
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should have a Ph.D. in experimental particle physics, excellent data analysis, computing and programming skills and the ability to carry out an independent research program. Prior experience with Machine
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should have a Ph.D. in experimental particle physics, excellent data analysis, computing and programming skills and the ability to carry out an independent research program. Prior experience with CMS will
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optimization. • Proficiency in programming (Python, R, Julia, or similar) and experience with frameworks like PyTorch, TensorFlow, or JAX. • Prior research in statistical learning and online optimization
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will also use these results to aid in the testing, evaluation and transition of technologies developed by government-funded research programs. Identify opportunities to apply AI to improve existing
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-footprint), emission models (e.g. IPCC, EMEP/EEA) and tools (e.g. Simapro); experience in programming languages (e.g. Python) to develop methods, models and tools is considered a strong plus. excellent spoken
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The successful applicant will hold a Ph.D. in agricultural economics or related fields. The successful applicant should have experience using statistical programming languages, such as R, Python, SAS, etc
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will also use these results to aid in the testing, evaluation and transition of technologies developed by government-funded research programs. Identify opportunities to apply AI to improve existing
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, and transport sectors (RENEW) Strong skills in implementing convex, linear, and mixed-integer programming and modelling frameworks (JuMP, Pyomo, Yalmip, GAMS, etc) Experience of working with future
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involved in analyzing data collected from the TexNet seismological monitoring program and other stations or assets that provide quality data. Comparing different methods and tools for moment tensor inversion