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View All Vacancies Chemistry Location: UK Other Closing Date: Sunday 12 May 2024 Reference: SCI266 Uncertainty quantification for machine learning models of chemical reactivity In this PhD
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quantitative methods, data skills and machine learning methods for effectively handling micro-level panel data, providing valuable skills for future careers. A Masters degree is not a prerequisite. Undergraduate
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. This project focuses on building confidence in ASL when coupled with our advanced machine learning tools for clinical application in dementia. Supervisor: Prof Michael Chappell Eligibility: https
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will be to shortcut the current search process in classify crystallographic orientation. This will be built upon where machine learning algorithms will be developed to extract material elasticity
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the areas of: Sustainable/resilient chemical supply chains Artificial Intelligence /Machine Learning for the prediction of bulk properties in performance molecules and materials Synthetic electrochemical
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leading-edge data analysis, including machine learning/AI approaches. We are looking to attract applicants from chemistry/materials chemistry with an innate drive and curiosity to deliver new solutions
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languages (Python/MATLAB) commonly used in machine learning applications is desirable but learning can be completed during the PhD. Excellent communication and interpersonal skills to facilitate collaboration
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, chemistry, or engineering. They should have evidence of strong background in optics, experience in computer programming, electronics skills, and be willing to learn/conduct cell and tissue-based experiments
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the areas of: Sustainable/resilient chemical supply chains Artificial Intelligence /Machine Learning for the prediction of bulk properties in performance molecules and materials Synthetic electrochemical
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if other criteria are met. Proficiency in programming languages (Python/MATLAB) commonly used in machine learning applications is desirable but learning can be completed during the PhD. Excellent