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Job Requirements REQUIRED: PhD in a field of quantitative science; strong background in machine learning and signal processing; experience with and commitment to rigorous and reproducible
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or optimization methods (e.g. Kalman filtering, convex optimization) Interest or experience in machine learning, particularly physics-informed or physics-constrained learning Programming and modelling skills
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Engineering at The Ohio State University conducts interdisciplinary research in scalable software systems, artificial intelligence and machine learning (AI/ML), edge-to-cloud computing platforms, digital
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FAccT and AIES), as well as policy fora. Applicants should have completed, or be near completion of, a PhD in Computer Science, Information Engineering, Statistics, Machine Learning or a closely related
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be responsible for designing and implementing physics-informed machine learning strategies for identifying constitutive laws in granular media. This includes the development of thermodynamically
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, chemistry, electrical and computer engineering, materials science and engineering, or computer science and engineering. The postdoctoral fellow is expected to actively work with more than one CQISE faculty
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Qualifications: Experience using the MOOSE simulation framework is highly desired. Experience fitting complicated physics-based models against test data, including machine learning and Bayesian optimization
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machine learning and optimisation to guide experiments and improve fungal process performance. design and undertake laboratory experiments with biological and analytical collaborators to validate
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machine learning or chemometric algorithms, LDIR, Py-GCMS and Raman, Caco-2 cells Additional Information Eligibility criteria Mandatory: *PhD in science, chemistry, food engineering or related disciplines
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develop automated workflows integrating artificial intelligence and machine learning (AI/ML) for the Center for Clean Hydrogen’s AEM Moonshot project. The project combines AI/ML, molecular simulation, and