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Field
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beam (FIB) is preferred Programming proficiency (e.g., Python) for experiment automation and/or image analysis is preferred Background in electronic, magnetic, or optical materials is preferred
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development, and publication in peer-reviewed venues. Strong background in machine learning, with research experience in deep learning, foundation models, or related areas. Solid programming ability in Python
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decision modelling optimisation, stochastic modelling, or control-inspired analytical approaches strong programming skills in Python and familiarity with modern computational toolchains a clear interest in
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methods. You possess excellent skills in your area of specialization. You have proven experience of working with microdata. You excel in using statistical software, such as Stata, R or Python. You have
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to collaborate; Very good command of the English language. These skills would be an advantage: Experience with Lagrangian transport models; Expert knowledge of programming, preferably in Python and/or Fortran
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possess extensive experience in developing deep learning algorithms Excellent programming skills (e.g., proficiency in Python, PyTorch, OpenCV) and experience in deep learning model optimization and
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datasets, and strong programming in Python and C. Familiarity with galaxy-redshift survey or 21-cm data analysis is a plus. The successful candidate will join the cosmology and astroparticle team at LAPTh
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learning and AI methods are preferred Strong skills in R and/or Python. An interest in conceiving and designing new projects, in line with the groups’ research focuses. A proven track record showing
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Education: PhD in Artificial Intelligence, Bioinformatics, Computer Science, Physics, Engineering, or a related field. Programming: Proficient in Python. Machine Learning: Strong experience with frameworks
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 months ago
reusable libraries in Python, Scala, and/or C++, taking advantage of Spark, Hadoop, and other tool stacks as appropriate. * Developing computational and algorithmic approaches to understanding