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University of Oslo and UiT The Arctic University of Norway | Norway, Michigan | United States | about 16 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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more information and how to apply: https://www.jobbnorge.no/en/available-jobs/job/306376/phd-research-fellow-in-machine-learning-for-cognitive-neuroscience Where to apply Website https://www.jobbnorge.no
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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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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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-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
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around specific video gaming topics. Moreover, the PhD student will complete natural language processing and machine learning research into AI-based activity planning and National Disability Insurance
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around specific video gaming topics. Moreover, the PhD student will complete natural language processing and machine learning research into AI-based activity planning and National Disability Insurance
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Dutch university requirements for entry into a PhD program. A background in machine learning, inverse problems, scientific computing, or related data-driven methods is highly desirable. You are curious
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machine learning and artificial intelligence For postdoctoral applicants: • A PhD in computer science, engineering, mathematics, or a related field, completed or expected by the starting date • A strong
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quantum attacks Verifiable and privacy-preserving machine learning (e.g. proving model integrity or fairness without revealing training data) Scalable ZK-proof constructions for federated and