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will apply machine learning — in particular physics-constrained symbolic regression — to discover compact analytical spin-Hamiltonians and their parameter dependencies. These Hamiltonians will feed large
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real-world challenges faced by industry, governments, and society within the international STRUCTURE project? Information The PhD candidate will work within the international research project STRUCTURE
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on advancing AI technologies for space systems with significant national and global impact. Visit the AU website to learn more about the School of Computer and Information Technology . Our people Our people
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; mechanism design and institutional design; cooperative AI and multi-agent systems; the study and development of large language models; cross-cultural psychology and large-scale behavioral data; philosophy
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DEI work here https://group.springernature.com/gp/group/taking-responsibility/diversity-equity-inclusion For more information about career opportunities in Springer Nature please visit https
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next-generation machine learning (ML) models that are both data-efficient and transferable, enabling more reliable catastrophic risk prediction, defined as the probability of exceeding critical safety
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mutations, etc.), analysis and integration of mass-spectrometry proteomics datasets, and artificial intelligence/machine learning (AI/ML) and systems-biology-focused efforts (i.e. large genomics and
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the discipline of bioinformatics, data analysis of large-scale (bio)medical data, applications of artificial intelligence and machine learning. You contribute to high-quality teaching in bachelor and master years
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science/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning
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/359354/2-phd-positions-on-learning-cau… Requirements Additional Information Website for additional job details https://www.academictransfer.com/359354/ Work Location(s) Number of offers available2Company