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authority. 2021: Nadine Akkerman Her publications on women's history, diplomacy and espionage in the early modern period are read by a wide audience and she uses innovative methods for her research. Nadine
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Apply now The Faculty of Science and the Leiden Institute of Advanced Computer Science (LIACS) are looking for a: PhD Candidate in AI for Network Analysis (1.0 FTE, 4 years) About this position
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gradient that determines atmospheric water transport to extra-tropical regions. To this end, you will generate proxy data to assess temperature, hydrology and carbon cycle information. Methods might include
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five Dutch universities: the University of Twente, the University of Groningen, Leiden University, Radboud University, and Delft University. The Formal Methods and Tools (FMT) group at the University
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PhD position within the project "What's wrong? Ancient corrections in Greek papyri from Egypt" (AnCo
regular meetings of the project research group; You will participate in the training programme of the LUCAS Institute, the Leiden Graduate School of Humanities, the National Research School in Classical
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embedded in the inter-faculty research team CHOIR (Centre for Healthcare Operations Improvement and Research). CHOIR is a research center within the UT that focuses on Operations Research methods
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field; Affinity with formal methods, including an understanding of basic of automata theory, formal languages, algorithms, and computability --- i.e., foundational computer science theory. Candidates with
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models. Requirements The successful applicants will have: A solid computational background, an interest in cognitive neuroscience a and strong deep learning programming skills. Ability to work in an
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communication skills are encouraged to apply. A MSc degree (or equivalent) in Mechanical Engineering, Computational Physics, Materials Science or a related discipline is required, with experience in atomistic
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Engineering, Computational Physics, Materials Science or a related discipline is required, with experience in atomistic modelling of materials and machine learning. Experience in atomistic modelling (molecular