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relevant research areas Proficiency in Python programming Excellent written and verbal communication skills in English Writing scientific papers and exploring new technologies. Application and Selection
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/ Deep Learning Knowledge of: Active learning, Bayesian optimization Reinforcement learning or decision-making systems Experience with: Python ecosystem (PyTorch, Scikit-learn) Data pipelines and
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cooperatively with others. Preferred Qualifications: • Proficiency in handling remotely sensed geospatial datasets and programming languages, particularly R or Python. • Knowledge, experience, and
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including statistical programming (e.g., STATA, RStudio, Python). You will demonstrate previous experience of contributing to publications/presentations as well as the ability to manage your own academic
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. Candidates must be supportive of the mission of the Land-Grant system. Candidates must also have a commitment to UF core values . Preferred: Experience with R/Python programing languages is desirable. Special
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scientific programming in Python and the use of frameworks such as PyTorch or TensorFlow. It is also necessary to have experience in federated learning and deep learning applied to medical imaging, as
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photometry and/or time-series analysis. Research experience in observational astronomy, demonstrated through publications appropriate to your career stage. Solid programming skills (e.g., Python) and
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computing (Linux) environments, including shell scripting. Strong programming and data analysis skills (e.g., Python, Fortran, R). Demonstrated ability of scholarly output (peer-reviewed publications and
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models and/or probabilistic modelling, and excellent programming skills in Python and a modern deep learning framework (e.g., PyTorch or JAX) are required. Excellent skills in spoken and written English
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proficiency in finite-element implementation through at least one of FEniCS/FEniCSx or Abaqus user subroutines (UEL/UMAT) in Fortran, together with scientific programming in Python. Knowledge, Skills