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developing new ones, including machine and deep learning methods. There will be opportunities for development of new cell line and animal models and testing, evaluation, and analysis of new genomic
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. Possible research topics include: Scientific machine learning Numerical partial differential equations (PDEs) Computational fluid dynamics Neural operators High-performance computing Data-driven
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to present their work and learn from others. Fellows are also encouraged to engage with colleagues and attend departmental seminars. Applicants must demonstrate outstanding scholarly achievement and promise
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opportunities for learning and professional development through structured, short-term appointments at Oak Ridge National Laboratory (ORNL) . The program is intended for individuals who seek to gain exposure
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simulation, machine learning, battery manufacturing, electrochemical systems, materials characterization, or computational mechanics A strong publication record, preferably including at least three first
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analysis, machine learning, and data-driven modeling applied to large astronomical datasets and time-domain surveys is highly desirable. Although experience with microlensing and transit exoplanet searches
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programming skills in modern languages such as Python or C++. Experience in statistical analysis, machine learning, and data-driven modeling applied to large astronomical datasets and time-domain surveys is
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years Required Related Experience Track record of scientific publications Required License/Registration/Certification N/A Physical Requirements Sitting at computer work station for long periods of time
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-quality datasets that are structured and annotated for simulation and machine learning. Develop Digital Twins and simulation pipelines for virtual validation and predictive performance assessment. Implement
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protocols using computer-based data collection software, preparing and submitting ethics applications, drafting informed consent documents, and pre-registering studies. - Submit research protocols