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4-year PhD fellowship in the Research Programme - Deep Learning-Accelerated Crystallography Pipeline
We welcome applications from candidates with a broad range of academic backgrounds and experiences for a 4-year PhD project on Mathematical and Machine Learning Aspects in Crystallography at Durham
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Optimization group within the Delft Institute of Applied Mathematics at TU Delft is offering a full-time PhD position in the area of combinatorics. During the PhD, you will work on topics at the intersection
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application! Your work assignments As a PhD student, you devote most of your time to doctoral studies and the research projects of which you are part. Your work may also include teaching or other departmental
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Job description The Discrete Mathematics and Optimization group within the Delft Institute of Applied Mathematics at TU Delft is offering a full-time PhD position in the area of combinatorics
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FieldComputer scienceEducation LevelMaster Degree or equivalent Skills/Qualifications Master’s degree in Computer Science PhD student in Computer Science, enrolled in a higher education in Romania. Good knowledge
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Kortrijk. Next to the research within your PhD, you will contribute to the education of our students by supporting lectures and lab sessions. For more information please contact Prof. dr. ir. Pieter Bonte
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statistics, mathematical modelling, statistical machine learning. Rules governing PhD students are set out in the Higher Education Ordinance chapter 5, §§ 1-7 and in Uppsala University’s rules and guidelines
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PhD Studentship: Robust, Certified, and Scalable Federated Machine Unlearning for Privacy-Preserving AI About the Project As federated learning systems become increasingly embedded in high‑stakes
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assistant positions that combine a PhD programme with a university teaching contract. Your time will be split approximately 60% on research and 40% on teaching. This provides excellent preparation
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wave equations. In the project, we will develop a new mathematical and computational framework that combines PDE-based modelling with ideas from data-driven reduced-order modelling. The aim is to obtain