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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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-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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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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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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. 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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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
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as a member of the GHER contributing to the EU research project COMEDI in a consortium of 11 leading partners in the field of data assimilation and deep learning. A successful applicant will develop
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of Health Science and Technology, one or more PhD stipends in Unsupervised Learning for Medical Image Analysis are available for appointment from November 1, 2026, or as soon as possible thereafter
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Project Scientists will engage in a variety of exciting learning opportunities and networking events. In addition to scientific mentorship, each participant will be paired with business advisors who can
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. In this PhD project, you will investigate foundation models for automotive imaging radar. The goal is to learn general radar representations from largely unlabelled data that can generalize across