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Field
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of positions: 1 Scientific Area: Electrical and Computer Engineering. Admission requirements: The candidate must be enrolled in a PhD program in Electrical Engineering at a Portuguese Higher Education
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scale radar datasets with high quality labels remain scarce. In this PhD project, you will investigate foundation models for automotive imaging radar. The goal is to learn general radar representations
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. As a PhD researcher, you will unravel the atomic-scale mechanisms of hydrogen embrittlement in compositionally complex recycled steels, using density functional theory and machine-learned interatomic
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. Applicants must have the right to work in the UK. We are unable to offer visa sponsorship for this role. Candidate requirements Candidates must have expertise in developing computer vision and machine learning
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to teach robots to understand forest well enough to navigate and move through them in real time, using machine learning on LiDAR point clouds and camera imagery for real-time understanding of the forest
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At the Faculty of Medicine, Department of Health Science and Technology, one or more PhD stipends in Human-Machine Interaction are available for appointment from October 1, 2026, or as soon as
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discipline. Essential Good programming skills, preferably in Python/C#. Experience with machine learning, deep learning, or experimental AI evaluation. Interest in secure distributed AI, federated learning
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AITHYRA GmbH - Research Institute for Biomedical Artificial Intelligence of the Austrian Academy of Sciences | Vienna, Virginia | United States | about 19 hours ago
Do you want to help transform human health through machine learning and life science approaches? Join AITHYRA in Vienna for a fully funded PhD at the intersection of machine learning, experimental
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automotive and aerospace electrification. Applications for this PhD position are invited at the Power Electronics and Machines Centre, University of Nottingham. Based in a recently built £18M facility
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focus on combining simulations using spatial-genetic-demographic individual based models (e.g., using the software SLiM), machine learning approaches, and genomic data to estimate larval dispersal