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present the results at national and international conferences, and contribute to the department’s teaching programs. Where to apply Website https://www.academictransfer.com/en/jobs/360204/team-hire-2-phd
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at Tilburg University, in collaboration with the Faculty of Military Sciences and the Joint Sigint Cyber Unit, invites applications for a fully funded postdoctoral position focusing on predictive modelling
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(HIMS), in close collaboration with industrial partner BOR-LYTE and Smart Industry testbeds. This position offers a unique opportunity to combine inorganic chemistry, spectroscopy, machine learning, and
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engineering Engineering » Mechanical engineering Researcher Profile Recognised Researcher (R2) Application Deadline 28 Apr 2026 - 21:59 (UTC) Country Netherlands Type of Contract Temporary Job Status Not
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for the efficient training and fine-tuning of machine learning models. The postdoc will closely collaborate with researchers at the Dutch Language Institute (and Radboud University Nijmegen). Selection Criteria PhD
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to apply Website https://www.academictransfer.com/en/jobs/359291/postdoc-in-machine-learning-and… Requirements Specific Requirements We will base our selection on the following components: a PhD degree in an
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, collaborate openly across institutions, and have the stamina to push through the engineering challenges that come with real-world physical AI. Your experience and profile: a PhD degree in Computer Vision
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multimodal modeling, interaction design, and adaptive systems. Job requirements We are looking for applicants who have or expect to receive a PhD degree before joining the project. Applications have a relevant
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conferences. support the teaching activities at the faculty (up to 10% of the time). What we ask of you A PhD in Machine Learning, Computer Science, Mathematics, Statistics, Physics or a closely related field
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. You have: A PhD in Computer Science, Machine Learning, Applied Mathematics, Scientific Computing, Data Engineering, or a closely related field. Demonstrated ability to conduct high-quality academic