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Field
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multimodal vision-language models for prompt-based 3D medical image segmentation Work with large-scale clinical CT datasets and scalable deep learning pipelines Validate models in close collaboration with
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methodologically strong and motivated to work at the intersection of applied machine learning, social sciences, and natural sciences. Essential qualifications: A completed PhD in data science, computer
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incremental optimization. We seek researchers to develop next-generation machine learning methods that fundamentally rethink how large-scale AI systems are trained, fine-tuned, and deployed. Our focus is on
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operational practices • Systematically exploring different formulations of mixed-integer constraints in grid optimisation problems • Developing machine learning models to accelerate mixed-integer
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Publish high-impact research in leading journals and present findings at international conferences on energy systems and machine learning Collaborate with industry partner to tackle challenges of practical
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workflows, including adaptive, automated, or agent-based (agentic) workflows that integrate simulation, data analysis, and/or machine learning. Experience with computational workflows on large-scale HPC
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of applying them to data. Collaborative endeavours with members of the IPMU and Oxford groups is highly encouraged. You will have the opportunity to teach. Applicants should have a PhD (or close to completion
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. The project focuses on developing an integrated approach that combines machine learning techniques with physics-based models to estimate the health of various system components. The aim is that fault diagnosis
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at the intersection of educational data science, AI in education, and the learning sciences, with additional advisory support from faculty and researchers across learning sciences, computer science, machine learning
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to completion) or possess equivalent research experience in a relevant computational field such as data science, artificial intelligence, machine learning, computer science or statistics. They will bring strong