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astrophysics, cosmology, or a related field completed by the start date; strong programming skills; working knowledge of machine learning applied to astrophysics and cosmology, in particular simulation-based
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assets include strong programming skills, experience with modern machine-learning techniques such as neural simulation-based inference or transformer architectures, and familiarity with fitting methods and
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school, and taking part in the supervision of students. A limited amount of teaching may be included (max 20%). Requirements PhD degree in machine learning, scientific computing, statistics, physics or a
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programming languages is required, and experience with the current ATLAS software, computing and particularly the tracking software would be particularly desirable. Experience with machine learning and
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of Research Experience1 - 4 Additional Information Eligibility criteria We are looking for a doctor in particle physics with less than two years of experience after the PhD. Experience in machine learning and
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Stanford University / SLAC National Accelerator Laboratory | Menlo Park, California | United States | about 2 months ago
Position Description The SLAC National Accelerator Laboratory (SLAC) is seeking a Research Associate (RA) to work on Machine Learning (ML) and Artificial Intelligence (AI) for high energy physics
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, for event reconstruction and classification, including potentially machine learning/AI Interpretation in suitable theoretical models Contribution to software activities that are required for wider use by DESY
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modern machine-learning techniques, will be exploited to improve the discrimination between the different polarization states. The analysis will use the complete Run 2 and Run 3 datasets collected by
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Searching for Triple Higgs Boson Production with the ATLAS Experiment at the CERN LHC School of Mathematical and Physical Sciences PhD Research Project Self Funded Dr T Vickey, Dr C Anastopoulos
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PhD candidate you will develop new ways to extract cosmic-ray physics from KM3NeT data. You will design and characterise reconstruction methods—both machine-learning-based and traditional