20 parallel-programming-"Multiple"-"Humboldt-Stiftung-Foundation" positions at Leiden University
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to you, we will look together at what you need, and draw up a development plan. This position is a good fit for you if you recognise yourself in the following: Completed PhD - not required at time of
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publications, and assisting in organizing and presenting at workshops and conferences; Take relevant courses and training; Participate in the PhD program activities and the intellectual life of the Institute
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training and supervision plan, including through the Graduate School; Selection criteria Master’s degree completed by the time of the appointment in Archaeology; Demonstrable ability and enthusiasm
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; Actively participate in discussions at the faculty, department, and research group level on research innovation; Follow PhD courses based on an individual training and supervision plan, including through
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on an individual training and supervision plan, including through the Graduate School; Selection criteria Master’s degree completed by the time of the appointment in Archaeology; Demonstrable ability and enthusiasm
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plan, including through the Graduate School; Selection criteria Master’s degree completed by the time of the appointment in Archaeology; Demonstrable ability and enthusiasm for innovative and inter
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; Solid background in Computer Science and Mathematics; Good programming skills in e.g., Python, C, C++; Experience with Cryptography; Experience with Machine Learning is a plus; Excellent written and oral
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will inform port operations in the Netherlands and globally, as part of the project objectives. Key responsibilities Develop own research plan (in alignment with the project and assisted by supervisors
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Strong programming skills in Python (please mention your level of proficiency and knowledge of other programming languages in your CV and motivation letter) Extraordinary motivation for scientific work
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the form of a research course report or master’s thesis; Good programming skills. Preferably you have provable experience with deep learning; software (e.g., Pytorch, Jax, Tensorflow) and large-scale