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Field
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programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Description The Laboratory for Computer-Human Intelligence (CHI-lab, https://www.x-labs.xyz ) in
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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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. Essential qualifications: PhD in Computer Science, Robotics, Machine Learning, or related fields. Programming skills in Python/C++ and experience with ML frameworks (e.g. PyTorch, TensorFlow, JAX). Strong
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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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Mississippi. Apply computer vision and machine learning approaches to integrate ground-based imagery, remote sensing data, and lidar data for high-resolution flood detection and mapping. Develop and calibrate
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
the group of Dr. Tengfei Li at the University of North Carolina at Chapel Hill. The successful candidate will develop and apply advanced statistical and machine learning methods for infant cognitive
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, and utilize it to develop a separation method. Your tasks will include: Performing computer simulations and matching them to experimental data Very close collaboration with experiments, including
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, excluding time associated with family planning, military service, illness or other life-changing events. Candidates must have completed all PhD requirements by the commencement of employment. Brookhaven
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transcriptomics data analysis. Experience in quantitative image analysis, computer vision, or digital pathology. A strong background in cancer biology or immunology. Experience with machine learning, deep learning
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to reconstruct subsurface defects; Implement image/signal‑processing or machine‑learning pipelines for automated flaw characterisation; Collaborate with the Federal University of Rio de Janeiro, including short