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Field
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approaches and will integrate novel hardware (including electrode arrays, microdevices, analytical systems) into automated robotic pipelines You will also apply machine learning-based analyses to imaging and
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). Prior experience in medical imaging & analysis and/or computer vision algorithm development using high performance computing (HPC). Publications in peer-reviewed technical/clinical journals or machine
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experiments, and machine learning (ML) to understand and predict multiscale transport phenomena in fuel cell systems. In particular, the postdoc will bridge pore-scale simulations and macroscale performance
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Are you interested in real-time distributed systems, IoT connectivity, and AI-driven automation? The Department of Electrical and Computer Engineering at Aarhus University invites applications for a
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objectives. For example, digital image processing tools, such as filtering or mathematical morphology, could be evaluated to extract structural elements of road edges from images. By combining spectral and/or
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Inria, the French national research institute for the digital sciences | Pau, Aquitaine | France | about 1 month ago
, loan of computer equipment, etc.) Social, cultural and sports events and activities Access to vocational training Social security coverage 2788€ / month before taxs Selection process Thank you to send
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psycholinguistics, brain-computer interfaces, robotics, cognitive modeling of language, computational linguistics, educational technologies, computational modeling of evolutionary and adaptive systems, image and
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for data analysis and system control. Experience with AI or machine learning in imaging is a bonus but not required. Ph.D. in Biomedical Engineering, Optical Sciences, Bioengineering, Physics, or a related
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learning and artificial intelligence Bachelor’s/ Master’s degree in computer science, mathematics, computer engineering, or relevant technical field First-author peer-reviewed published papers (or under
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control) the arrangements of cells in spheroids and tumoroids. The project will primarily involve the development and testing of machine learning techniques based on biophysical simulations to predict