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. Experience with Python programming. Familiarity with machine learning methods. Strong communication skills and ability to work collaboratively across theory and experiment. Desired Qualifications PhD in
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(e.g. Interspeech, ICASSP, SSW) and contribute to open-source release of corpus and models. Qualifications Requirements A doctoral degree in speech technology, machine learning, computational linguistics
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middleware (e.g., ROS, MoveIt) and hardware integration. Knowledge of machine learning, reinforcement learning, or vision-language models for robotics is a plus. Hands-on experience with robotic arms (e.g
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, volumetric data analysis, optimization methods, statistical modeling, or machine learning for scientific applications. Prior experience with cryo-EM software frameworks or structural biology data is considered
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insurance, generous paid leave and retirement programs. To learn more about USC benefits, access the "Working at USC" section on the Applicant Portal at https://uscjobs.sc.edu. Research Grant or Time-limited
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materials using statistical mechanics, molecular simulations, and machine learning. Expectations Candidates will be responsible for: Developing multi-scale modeling methods for polymeric materials, using
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data output by using appropriate computer language/tools to provide technical solutions for moderately complex application development tasks. Document code and associated processes by adhering
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/10.1016/j.xcrp.2022.101112 and https://doi.org/10.1080/08940886.2022.2114716 key words synchrotron radiation; X-ray Absorption Spectroscopy, machine learning, artificial analysis, autonomous experimentation
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Qualifications: M.S. (or equivalent professional degree) in physics or a related field, and experience with different computer environments and languages especially Python, Matlab, IDL, FORTRAN and UNIX/LINX
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strengths of the University of Tübingen in Computer Sciences and Machine Learning. Potential research directions include, but are not limited to, phylogenetic, demographic, ecological and biogeographic