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Field
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? Join us to develop deep learning techniques for fusing acoustic sensor data with other vehicle sensors for robust multi-modal environment perception. Help shape the future of autonomous driving! Job
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Science. Commitment to undergraduate and graduate education. Demonstrated expertise in machine learning/deep learning and software development (Python; PyTorch/TensorFlow). Peer-reviewed publications and strong
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., RGB-D, thermal, LiDAR, tactile), universal perception frameworks (e.g., Vision-Language-Action (VLA) models, Transfer Learning, self-supervised learning) that generalise across tasks and scenarios in
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behavior. (2) Evaluate their effects on performance, safety, and security metrics. (3) Propose and validate mitigation and hardening techniques at the model, system, and learning levels. The targeted
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probabilistic frameworks. Experience with machine learning or AI methods for localization or perception (e.g. learning-based SLAM, data-driven sensor fusion) is a plus. Underwater or field robotics experience
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discipline (computer science, neurosciences, biology, cognitive sciences) • Skills in perception psychology, neurosciences, machine learning, tracking technology and/or computer graphics as well as interest in
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equivalent university degree in psychology or a related discipline (computer science, neurosciences, biology, cognitive sciences) • Skills in perception psychology, neurosciences, machine learning, tracking
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(including ultra-high-field and ultrafast MRI) Computational and network neuroscience Machine learning and biologically inspired AI Vision science and predictive coding Clinical neuroscience and
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PI & HOSTING GROUP The direct responsible persons for this post are Dr. Joost van de Weijer and Bogdan Raducanu, members of the Learning and Machine Perception (LAMP) team at the CVC. For more
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urban design with microclimate simulations and measurements, GIS and Digital Twin technologies, and machine learning. The work will be part of a Horizon pilot project aimed at realizing a scenario-based