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/linguistically-motivated-language-model-security/ , and an Erasmus+ project on AI-based Personal Language Learning Assistants in collaboration with VSKDansk https://www.brondby.dk/om-kommunen/nyheder/2025/08
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 1 month ago
concepts into accessible, domain-relevant learning experiences for students. The full job description is available here: https://computing.mit.edu/lecturer/ Job Requirements REQUIRED: PhD in Computer Science
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modern, project-based learning, catering for the needs of our students and the industries in which they will work. As a new department we have invested in new laboratories and maker space at the centre
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collaborative and stimulating environment? We offer a PhD position in our diverse research team in the Laboratory of Microbiology at Wageningen University & Research. As a PhD candidate, you will investigate how
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-content microscopy and AI/deep-learning-supported image analysis will be used to quantitatively assess neuronal morphology and maturation. Parameters will include dendrite length and branching, axon growth
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areas within engineering education; we are particularly interested in those whose research uses machine learning, deep learning, large language models, generative AI, or advanced quantitative methods
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methods tailored to ILC. The PhD researcher will fine-tune and benchmark pathology foundation models using multi-site H&E and immunohistochemistry whole-slide images. The aim is to learn representations
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. Kindly submit your CV and motivation letter to the attention of Mr. Menno Koeslag, HR Advisor. Where to apply Website https://www.academictransfer.com/en/jobs/364035/phd-candidate/apply/ Requirements
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spouses, please visit the Office for Faculty Equity and Welfare's website . Qualifications Basic qualifications (required at time of application) PhD (or equivalent international degree), or enrolled in PhD
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qualifications include: Strong research experience in deep learning and foundation models, including experience with pre-trained models, fine-tuning, transfer learning, or self-supervised learning. Experience with