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years is to be expected. A PhD training programme is part of the agreement and the successful candidate will be enrolled in the Graduate School of Law. Do you meet the qualification criteria? If yes, your
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There is an increasing demand for infrastructure services while, at the same time, infrastructure networks reach capacity thresholds. Extending and upgrading infrastructure is challenging and limited not only because of long infrastructure lifetimes coupled with high investment costs but also...
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Vacancies PhD position AMBITIONS: accelerating transitions by navigating values in the built environment Key takeaways Circularity, energy transition, extreme heat, and flooding: all potentially
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Faculty of Science The Faculty of Science (FNWI), part of Radboud University, engages in groundbreaking research and excellent education. In doing so, we push the boundaries of scientific knowledge and pass that knowledge on to the next generation. We seek solutions to major societal challenges,...
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Vacancies PhD position in Design of Forward and Reverse Supply Chains for Additive Manufacturing in Maintenance Key takeaways We are seeking a highly motivated and curious PhD candidate
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to indicate that a successful completion of the PhD thesis within the next three years is to be expected. A university PhD training programme is part of the agreement, and the candidate will be
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Vacancies PhD positions (3x) on RF/Microwave Gallium Nitride (GaN) MMIC design and test Key takeaways Gallium Nitride: covering the entire chain from device modelling and characterisation, MMIC
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PhD in Aneuploidy in Mammalian Embryos / Clinical Veterinary Embryologist Faculty: Faculty of Veterinary Medicine Department: Department Clinical Sciences Hours per week: 36 to 40 Application
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Published Monday 11 Aug 2025 Deadline Monday 22 Sep 2025 Work area PhD Organisational unit Erasmus School of Law (ESL) Salary € 3.059 - € 3.881 Employment 1 fte - 1 fte Introduction Are you curious
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you eager to make AI more sustainable? As a PhD Candidate, you will develop innovative methods for predicting and reducing the energy consumption of large-scale AI systems during their design phase