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to enroll in a PhD program Preference will be given to candidates with knowledge and skills in the following areas: machine learning and neural networks, including LSTMs and explainable artificial
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Areas: computer science; applied math; artificial intelligence; machine learning; statistics; engineering Appl Deadline: 2026/02/06 11:59PM (listed until 2026/06/19) Fellowship Description: Apply
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gravitational-wave astronomy. The successful candidate will join Greg Ashton’s STFC-funded programme, Advancing Gravitational-Wave Astronomy Using Artificial Intelligence, to work on computational Bayesian
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CeMM - Research Center for Molecular Medicine of the Austrian Academy of Sciences | Austria | 16 days ago
the PhD Program please visit the website: https://apply.cemm.at/ Where to apply Website https://apply.cemm.at/ Requirements Research FieldBiological sciences » OtherEducation LevelMaster Degree
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of the project Teaching commitment: 4 hours of teaching per week Your profile: Master’s degree or PhD in Electronics/ Electrical Engineering/ Informatics/Physics or related disciplines Intention to graduate as
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space-based systems, including large satellite constellations. A recent PhD in physics, engineering, computer science, or other relevant fields and strong interest in technical and policy research
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description We welcome your application for a PhD research fellow position within
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These efforts aim to enable future ultra-low-latency applications (e.g., cloud gaming, latency-critical workloads) and contribute to next-generation Internet design. Qualifications PhD in Computer
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professors, 2 postdoc researchers, and about 20 PhD students. The research for these PhD positions will be conducted in the System Software team, headed by prof. Bjorn De Sutter (https://users.elis.ugent.be
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: Education: Bachelor in Biosciences, or Engineering degree in Computer or Data Sciences. PhD in bioinformatics, data sciences, machine learning or related areas. Experience: previous experience working with