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announces an open competition for the position Ph.D. student - Machine learning-based tools for multiparametric enzyme optimisation Workplace: RECETOX, Faculty of Science, Masaryk University in Brno, Czech
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, mathematics, engineering, natural sciences or other data science/machine learning/AI related disciplines Language requirements English C1 or equivalent Application deadline January, please see website for exact
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campus partners, the Assistant Director for AI and Learning will design, develop, and implement programming and resources to 1) support faculty and instructors in articulating and implementing effective
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machine learning, distributed systems, programmable hardware, statistics, and applied mathematics. Our culture is steeped in the idea that we will never stop solving; we’re looking forward to supporting
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retirement programs. To learn more about USC benefits, access the "Working at USC" section on the Applicant Portal at https://uscjobs.sc.edu. Position Description Advertised Job Summary Teaching duties will
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into reliable information about structural and aerodynamic behaviour remains a challenge. The PhD will develop data-driven methods that combine measurements, physics-based models, and machine learning to extract
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quantitative or computational approaches are required. Prior experience with image analysis, machine learning, signal processing, or structural biology is meritorious but not mandatory. Excellent written and
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Modelling, Applied Statistics, Linguistics, Data Analysis, Large Language Models, Machine Learning. Start date: 1st October 2026 Deadline: 30th April Duration: 36 months Funding: Funded Funding towards
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imaging analysis techniques in biological systems, including cell and tissue samples. • Familiarity with conventional and machine learning based image processing approaches. • In-depth knowledge
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computing, and to pursue new strategic research initiatives under the Department/Faculty/University. Requirements: Applicants should possess a PhD degree in Computer Science, Computer Engineering, Information