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ICT Services & Applications. Your role The successful candidates will join the Computer Vision, Machine Intelligence and Imaging research group, led by Prof. Djamila Aouada, to conduct research in
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, validation, deployment, monitoring, and (preferably) MLOps practices. You can develop and integrate AI models into operational systems, including: user‑facing applications (computer vision, sensor fusion
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radio access and/or core network standards, implement the features in MATLAB and/or OpenAirInterface software for computer simulations, and conduct theoretical research to submit proposals to the ongoing
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information and communication experts, linguists, computer engineers, lawyers and economists, and offers adapted solutions to issues relating to the intelligibility of digital data and their standardized
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experience with PyTorch. You have a track record of publishing in top image processing / computer vision journals. Your research qualities are in line with the faculty and university research policies . You
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with Artificial Intelligence and deep learning concepts for robotics computer vision, tactile sensing, reinforcement learning Experience with robotic simulation tools e.g., ROS, Gazebo, Mujoco, IsaacSim
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discussion. The position comes with travel and computer equipment resources, and other benefits. The postdoc will join a vibrant research group at Ghent University, and will be part of an active international
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outreach activities, including engagement with students and the broader public For further information, please contact Prof. Eva LAGUNAS at . Your profile Ph.D. in Electrical/Computer Engineering, Computer
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Computer Engineering The candidate should have published at least 3 journal papers in top 10% international journals of the field, and/or multilateral project experience and/or relevant industry experience
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representation learning. You have strong programming skills, especially in Python, and preferably experience with PyTorch. You have a track record of publishing in top image processing / computer vision journals