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position with the possibility of extension. For more details on our research and recent publications, see the Geometric Machine Learning Group’s website: https://weber.seas.harvard.edu For questions, please
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requirements of Monash for PhD (https://www.monash.edu/graduate-research/future-students/apply); Knowledge of machine learning (would be a plus if familiar with NLP and Medical AI); Knowledge of cybersecurity
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University of Oslo and UiT The Arctic University of Norway | Norway, Michigan | United States | about 4 hours ago
/61927781301 Application and more information: https://www.jobbnorge.no/en/available-jobs/job/303593/phd-fellowships-in-knowledge-driven-machine-learning-in-norway-8-positions Keywords: Bayesian
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professional network between PhD-students, researchers and industry. Read more: https://wasp-sweden.org/graduate-school/ Your work assignments Machine learning, and in particular deep learning, requires
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Primary supervisor David Dowe Co-supervisors Dr Nenad Macesic Research area Machine Learning Antimicrobial resistance (AMR) is one of the most significant and immediate threats to health in Australia
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Primary supervisor David Dowe Co-supervisors Dr Nenad Macesic Research area Machine Learning Antimicrobial resistance (AMR) is one of the most significant and immediate threats to health in Australia
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Doctoral Researcher in Trustworthy Machine Learning Employer AALTO UNIVERSITY Location Finland (FI) Salary 3168 EUR per month Closing date 31 Oct 2026 View more categories View less categories Job
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Researcher in Trustworthy Machine Learning Your role and goals Trustworthy & Adversarial Computing Lab ( https://taclab.aalto.fi ) led by Sebastian Szyller is looking for a doctoral researcher (PhD student) to
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, and physics-informed machine learning open new possibilities for establishing grid foundation models. However, it is still unclear which model structures is most capable and suitable, how well they
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-supervised machine learning methods for biological data where reliable labels are scarce, expensive, or impossible to obtain. The aim is to train models on synthetic data generated by biophysical