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goal of your PhD project will be to develop combined X-ray and visible-light imaging technologies and machine learning methods for high-throughput inspection tasks. The work will include the development
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or environmental engineering, Mathematics (Operations research) or Computer Science or Machine Learning). Documented knowledge of relevant methodologies, both quantitative and/or qualitative, at master’s level
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), Computer Science (Machine learning, Efficient Algorithms and High Performance Computing), and Physics (Image Formation Modelling). Your project is part of the DUAL-IMPACT project, which focuses on the development
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chemistry and study the physicochemical properties of peptides loaded into the materials. Build surrogate models and apply machine learning techniques to extract design rules and rapidly screen thousands
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student and postdoc who will carry out experimental work, which will allow for validation of the models. You will collaborate with experts from TU Delft, TNO and several large and SME companies involved in
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applications based on the following skillsets. If you do not fullfil all requirements, please still apply (there is room for learning-on-the-job). An engineering degree, preferably, aerospace or mechanical
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meet the requirements for admission to the faculty's doctoral programme in Engineering Cybernetics . Strong programming skills, in particular Python, and practical experience with modern machine learning
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independently. We value personal development: you will receive training in advanced computational techniques, machine learning, data analysis and scientific communication. You’ll have the opportunity to attend
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computer simulations Working in close collaboration with theoretical physicists to test models and develop new insights Your Profile Bachelor's degree in physics Master’s degree/diploma in condensed matter
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Hey machine learning enthusiast with a love for physics and