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data annotation (brat), Python scripting, and integrating optimization techniques such as fine-tuning, Chain-of-Thought, and Retrieval Augmented Generation to enhance LLM outputs. Experience in creating
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scientific discipline. Experience in developing and running an atmospheric transport model. Experience in interpreting data. Experience with FORTRAN and Python computer languages. Proven ability to work
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implemented in the Fortran programming language, and it relies on the platform CUDA for parallelization of the computation over several GPUs’ cores, and has interfaces with Matlab and Python for ease of use
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ethical frameworks. Proficiency in Python and experience with relevant libraries for AI/ML development. Experience with advanced AI methodologies including deep learning, transfer learning, and neural
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preferred. Proficiency in computational tools such as MATLAB, Python, R, or machine learning applications in immunology is desired. Candidates should have a PhD in Chemistry, Chemical Engineering
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appreciated Ability to engage with interdisciplinary teams involving statisticians, computer scientists, and climate experts Programming experience in a scientific computing language (e.g., Python, Fortran
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experiments Documented expertise in data analysis using scripting (ideally python-based) Experience carrying out SAXS/WAXS experiments at large scale facilities Working experience with soft matter and/or
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Environmental Engineering or a related field High proficiency in programming languages (e.g., Python or MATLAB) A strong record of authorship in peer-reviewed academic journals Excellent interpersonal and
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to develop robust experimental designs, to conduct data processing and statistical analysis with relevant programming language(s) (such as Python and R), and to handle high-dimensional research data to a high
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learning methodologies. The underlying data are complex and will require sophisticated data management and integration skills. A candidate should have proficiency with GIS software and Python, strong written