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(CALS), and our management and staff are committed to upholding the highest standards of ethics and excellence across all aspects of our work. This position will be based in the K. Lisa Yang Center
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90 are PhD students, and about 40 % of all employees are internationals. In total, it has more than 600 students in its BSc and MSc programs, which are based on AAU's problem-based learning model
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grant that focuses on modeling spatio-temporal medical image analysis with a particular focus on learning from limited labelled data. Here you will be a part of the UiT Machine Learning Group and will
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priority is maturing infrastructure and operating models to support NIST 800-171 compliance, including identity and role-based access management, physical and logical access controls, secure facilities and
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libraries (spaCy, NLTK, Hugging Face). Machine learning fundamentals and model evaluation. API integration and version control (Git). Prototyping, testing, and refining NLP-driven applications. Applying human
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interns, modeling safe and professional art-handling practices. Access & Outreach: Facilitate research inquiries, support image requests/permissions, and provide service to collection stakeholders. Author
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activities, without formal teaching duties. Specialists apply advanced expertise in areas such as neuroimaging, electrophysiology, molecular neurobiology, or computational modeling to advance the university’s
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thinking, and has a strong background in cognitive/computational neuroscience or related fields. Relevant areas of expertise include neuroimaging (fMRI), behavioural testing, and computational modelling
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further development of the complex research and treatment infrastructure (including control areas, a theranostics unit, and GMP-compliant facilities) and to ensure their usability from both a technical and
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transfer This research combines advanced numerical simulation and artificial intelligence to develop predictive models for high-temperature multiphase flows, with specific relevance to steel casting