15 learning-"https:" "https:" "https:" "https:" "https:" "https:" "UCL" PhD research jobs in Netherlands
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Apply now » Internal Research Fellow (PostDoc) in Theory of Deep Learning Job Requisition ID: 20829 Date Posted: 5 August 2026 Closing Date: 2 September 2026 23:59 CET/CEST Publication: External
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European Magnetism Association EMA | Nijmegen, Provincie Gelderland | Netherlands | about 2 months ago
Home > JOBS Ultrafast Magnetism in Frustrated Materials [ All offers ] 2026-07-24 | PhD Lab/Company : HFML-FELIX Location : Nijmegen, Netherlands Yearly income : https://www.nwo-i.nl/en/vacancies/phd
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, and bring together technology and service providers, customers, researchers, investors and institutional partners. You can read more about BSGN on the website: https://bsgn.esa.int You are
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Eindhoven University of Technology (TU/e) | Eindhoven, Provincie Noord-Brabant | Netherlands | about 2 months ago
high demand. You will be supervised by Dr Gabriele Liga ( https://www.tue.nl/en/research/researchers/gabriele-liga ) and Dr Yunus Can Gültekin at TU/e. You will carry out two research secondments: one
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Are you fascinated by how curiosity shapes learning in the classroom? Would you like to investigate how children seek information, explore and learn in real-world educational settings? If so, then we
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collaborative environment in which you have the freedom to work independently, while also contributing to a shared goal. You take a mastery-oriented approach to your own learning and development and are eager
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characterize subsurface structures, buried infrastructure, and material properties; · develop and apply AI and machine learning methods for signal processing, image analysis, data fusion, and prediction
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gratis e-learning carrière oriëntatie! Start de e-learning Do's & don'ts bij solliciteren De beste tips over solliciteren. Wees klaar voor je sollicitatiegesprek. Ontdek de do's & don'ts Schrijf je in
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plant growth, physiology and disease development; profile root and leaf microbiomes using amplicon sequencing; analyse integrated microbiome and phenotyping datasets; contribute to machine-learning
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structural causal models. We further propose to develop scalable structure learning methods for these new models, including latent variable identification, and to demonstrate their effectiveness on real