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and machine learning algorithms to analyse and interpret the acquired data. The successful candidate will work primarily with Dr Qimei Zhang in the Department of Engineering at the Nottingham Trent
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approaches to understand cancer treatment toxicity and improve patient care. Enthusiasm for learning new methods and collaborating across disciplines. Candidates are not expected to have experience in all
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• PhD in Mathematics/Statistics/CS. Skills: • Proficiency in reinforcement learning, quantitative finance, risk management, and machine learning. • Strong programming skill in Python
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to groom the next generation of leaders, thinkers, and innovators to thrive in the digital age. Located in the heart of Asia, NTU’s College of Computing and Data Science is an exciting place to learn and
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are built on a foundation of continuous learning and growth . We actively support professional development by providing all full-time staff employees with at least 80 hours of paid time per year and provide
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, data processing pipelines, machine learning and generative AI applied to physical activity and sleep research. The position will contribute to the development of an AI-based behaviour change tool and
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to groom the next generation of leaders, thinkers, and innovators to thrive in the digital age. Located in the heart of Asia, NTU’s College of Computing and Data Science is an ‘exciting place to learn and
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Center for Drug Evaluation and Research (CDER) | Silver Spring, Maryland | United States | about 19 hours ago
therapeutics and generic drugs. This effort covers more than just medicines. Research Project: Under the guidance of a mentor, you will engage in a hands-on learning experience involving Cell-free protein
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-guided learning activities may draw upon biomonitoring projects associated with CDC’s National Health and Nutrition Examination Survey (NHANES) and similar epidemiological studies of the U.S. population
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The Research Fellow (RF) will conduct research in the field of coastal dynamics, with a focus on the development and application of machine-learning-enhanced coastal models. The project aims