262 phd-studenship-in-computer-vision-and-machine-learning PhD positions in Netherlands
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PhD Position: Activating Heritage as a Mediator for Dialogue and Belonging in an Era of Polarization
successful completion of the PhD thesis within the contract period is to be expected. A PhD training program is part of the agreement and the successful candidate will be enrolled in the Graduate School
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vision on the structure of the PhD and will have the freedom to reshape the research projects within the general boundaries of the subject of spatial and foraging ecology and habitat use of Black-tailed
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the Mobilization of Conflict Narratives on Social Media” and contribute to teaching in selected BA/MA programmes at the Faculty. The position also includes an extensive training programme. The PhD candidate will be
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. The position also includes an extensive training programme. The PhD candidate will be supervised jointly by associate professor Mike Huiskes, assistant professor Joanna Chojnicka and assistant professor Dimitris
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learning approaches and contributing to the development of better (bio)catalysts and drugs? We are offering three fully-funded, 4-year PhD positions at the University of Groningen or the Technical University
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observations. Your major challenge is in model development, and there is room for you to develop machine learning applications in the field of firn modelling. If successful, your work will lay the foundation
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, with the following qualifications: a Master's degree in Computational Biology, Applied Mathematics, Physics, Computer Science, or a related field; enthusiasm for learning the biological background and
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PhD Candidate in Transnational Retirement across the United Kingdom and India Faculty: Faculty of Geosciences Department: Department of Human Geography and Spatial Planning Hours per week: 36
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background in cosmology, astrophysics, physics, mathematics and/or computer sciences, who seek to obtain a Joint-PhD degree from the University of Groningen and Tartu Observatory. To be eligible to apply, a
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(+/–) applied to the electrodes can be optimised in a machine-learning approach to optimise the gel morphology and the embedded stimuli-transduction pathways towards the targeted mechanical behaviour. Departing