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Field
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psychology Experience with quantitative research methods Experience with R, Python, Javascript/HTML/CSS, or other programming languages Experience managing a team Other Requirements: Regular weekend
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related field Basic understanding of power-system dynamics, stability, and inverter-based resources Experience or familiarity with DIgSILENT PowerFactory, PSCAD, PowerDynamics.jl, or Python Analytical and
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or learning-based control; diffusion policies or generative decision-making; robotics physics simulators (e.g., Isaac Gym, MuJoCo, Gazebo) Proficiency in Python and/or C++ Experience with PyTorch or similar
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Python and common machine learning / NLP libraries (e.g., PyTorch, scikit-learn) Good written and oral communication skills; proficiency in Mandarin Chinese so as to support the technical components
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developing quantitative evaluation methodologies or performance metrics. Strong programming skills in Python and modern AI frameworks (e.g., PyTorch, Hugging Face). Experience with optimization algorithms
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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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outputs. Proficiency in scientific programming and computational tools commonly used in numerical modeling and environmental data analysis, such as Python, MATLAB, Fortran, C/C++, or comparable languages
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independently and as part of a collaborative research team. Preferred: Familiarity with relevant software packages and/or programming languages, such as Bruker TopSpin, Varian VnmrJ, Mestrelab Mnova, and Python
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Classification Title: Post-Doctoral Associate in unsupervised and generative ML for chemistry Classification Minimum Requirements: PhD in Chemistry or related area Strong coding (Python) and
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DAS datasets, including signal processing, quality control, feature extraction, system identification, imaging, inversion, or source characterization, is highly desirable. Proficiency in Python, C