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10 Jul 2026 Job Information Organisation/Company KU LEUVEN Research Field Language sciences » Languages Language sciences » Linguistics Educational sciences » Teaching methods Researcher Profile
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histopathology and clinical information to learn robust cross-modal representations for diagnostic prediction. It pursues two integrated objectives: (i) to develop generative and explainable AI approaches
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, both spoken and written. You will receive a PhD scholarship to join our lab and pursue your doctoral studies. Initially, this full-time position is offered on a one-year contract, with the possibility
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activities, including seminars, workshops, and national and international conferences. Publish research findings in high-quality peer-reviewed academic journals. Contribute to the teaching of undergraduate and
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(experimental) solid state physics. You obtained excellent results in your prior studies and are fluent in both spoken and written English. You are a team player who actively shares experience and knowledge with
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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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, conference participation and coursework, and contribute to supervision and limited teaching activities related to the research. You hold a Master’s degree with strong academic performance in Civil Engineering
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://gbiomed.kuleuven.be/english/phd ). •Extensive opportunities for professional development, including international conferences, publications and workshops. •Access to a wide range of training, education and career
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time (no more than one day a week) to teaching activities or supporting assignments, and take up exam invigilator tasks. We are looking for a candidate with the following qualifications and capacities
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, fluid mechanics, simulation, and programming (Fortran, C/C++, Python, …). Proficiency in English is a requirement. The position adheres to the European policy of balanced ethnicity, age and gender