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Field
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The GW4 BioMed3 Doctoral Landscape Programme is offering up to 18 MRC funded studentships across a range of biomedical disciplines, with a start date of October 2027. These four-year studentships
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spectrum of topics in CNU sections 27 "Computer Science" and 61 "Computer Engineering, Automation and Signal Processing". The laboratory is located at the heart of the Sophia Antipolis technology park, in a
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robotic platforms, integration of optical and imaging-based feedback, and development of modeling and control strategies for operation in complex biological environments. Particular attention will be given
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Science (Information and Signal Processing), Speech, and Cognition. The unit conducts its research through 11 teams organized into 4 research departments: - Automation and System Safety - Data Science
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18 Sep 2026 Job Information Organisation/Company KU LEUVEN Research Field Computer science » Informatics Engineering » Biomedical engineering Engineering » Computer engineering Biological sciences
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Signal processing and data analytics, control systems Machine learning or computer vision Product development in multidisciplinary engineering teams. Responsibility teams Exposure to globally leading
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Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. | Dortmund, Nordrhein Westfalen | Germany | about 7 hours ago
, computer science, medicine, pharmacology, and physics. ISAS is a member of the Leibniz Association and is publicly funded by the Federal Republic of Germany and its federal states. In the department
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Engineering, Electrical Engineering, Medical Imaging, Applied Physics, Computer Science, or a related field. Strong affinity with ultrasound imaging, signal processing, medical image analysis, or computational
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programme comprises both research and coursework. As a doctoral student, you will gain broad research experience in forage and crop science. Your main activities will include: planning and conducting field
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integrating AI with vision at the edge. Despite recent advancements, the synergy between AI and computer vision remains constrained by fundamental imaging bottlenecks. Conventional HDR techniques frequently