16 parallel-and-distributed-computing positions at University of Twente in Netherlands
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Vacancies PhD Position on Neural Engineering/Computational Neuroscience/Biomedical Engineering Key takeaways We are seeking a highly motivated PhD candidate to join a multidisciplinary team working
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terms of technologies, infrastructures, (geo)spatial distribution, scale and related investment needs matters for how and which stakeholders are affected, who gains or loses power, and how benefits and
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MSc courses in Civil Engineering, and operates across the three main themes of Construction, Transport and Water. The Department has around 50 academics distributed over nine Chairs. About the
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your project, you will address issues such as data privacy and ownership, distribution of responsibilities and accountability, and access to technology at organisational, project, and district levels
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. About the organisation The faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) uses mathematics, electronics and computer technology to contribute to the development of Information
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534892715. Screening is part of the selection procedure. About the organisation The faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) uses mathematics, electronics and computer
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at the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), University of Twente in Enschede, The Netherlands, and several experienced supervisors (Dr. Ray Hueting, PE group and Prof
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learning and team learning in the HRD track of our Master and pre-master program Educational Science and Technology (EST). You will also contribute to the challenge-based interdisciplinary minor
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. The intended starting date is around April 1, 2026. Screening is part of the selection procedure. About the organisation The faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) uses
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: Deep learning Medical image computing (preferably x-ray imaging) Computationally efficient deep learning Deep learning model generalisation techniques Translating deep learning models into clinical