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declaration of non-extension. With appropriate work progress, an extension to a total maximum of 4 years is possible. About the team Join the Responsible Machine Learning (ML) Group at the Faculty
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they bond in materials, but also develop transferable skills in scientific computing, data analysis and visualisation. "Machine learning for atomic-scale structure determination in thick nanostructures" (with
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for the scholarships. Experience in Machine learning and/or finite element modelling would be preferable. The successful candidate will be supervised by Associate Prof. Hafizah Binti Ramli, Dr Sabrina Fawzia, and
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Are you interested in developing new image analysis and machine learning methods for cancer diagnostics and clinical decision support? Would you like to work in a multidisciplinary team together
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is looking for an aspiring PhD candidate to research causal machine learning and uncertainty quantification for Earth Observation time-series. Currently, predictive AI in Earth Sciences relies heavily
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of material behavior to the development of the material to the finished component. PhD position on physics-based machine learning modeling for materials and process design Reference code: 980 - 2026/WD 1
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reinforcement learning methods can be used to solve multiobjective discrete and combinatorial optimization problems. The goal is to develop new algorithmic approaches that combine ideas from machine learning
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to defining specifications and requirements for the FDI system • Development and evaluation of model-based and machine learning-based FDI algorithms, in close exchange with relevant stakeholders • Close
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analysis Background in biomedicine and digital pathology What we offer Embedding within a computational team, with extensive experience in computational biology and machine learning. Embedding within
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machine intelligence into working AI solutions? We have launched a pioneering research and innovation hub in AI—one that will shape the way humans and machines collaborate for decades to come. Led by Prof