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Field
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Solubility, and dissolution dynamics, are key physico-chemical properties of active ingredient solid forms and are required across the chemical industry, however their determination remains a
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integrates machine learning and statistics to improve the efficiency and scalability of statistical algorithms. The project will develop innovative techniques to accelerate computational methods in uncertainty
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in our CDT program, and warmly encourage applications from students of all backgrounds, including those from underrepresented groups. We particularly welcome students with disabilities, neurodiverse
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should have a strong mathematical background, particularly in dynamical systems theory, and a keen interest in network science, and scientific computation. The student will gain invaluable experience
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of tomorrow and creating novel solutions to major global challenges. Our community is made up of 120 nationalities, 14 000 students, 400 professors and close to 5000 faculty and staff working on our dynamic
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Computing Strategies: Develop algorithms that dynamically adjust computing resources to minimize energy usage without compromising performance. Thermal Management Techniques: Design systems that effectively
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changes (so called swelling). Swollen batteries are at risk of rupturing which may significantly shorten their lifetime. Development of advanced computer models is critical for understanding and
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We are seeking a highly motivated candidate to undertake a PhD program titled "3D Temperature Field Reconstruction from Local Temperature Monitoring in Directed Energy Deposition." This exciting
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will dynamically adjust turbine parameters such as yaw, pitch, and torque to maximize Annual Energy Production (AEP) while minimizing component stress. Additionally, a hybrid predictive maintenance model
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create a computational tool based on experimental input, simulated data, and machine learning methodology to extract 3D atomic structure information from 2D identical location STEM images. STEM image data