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Max Planck ETH Center for Learning Systems | New Germany, KwaZulu-Natal | South Africa | 26 days ago
requirements, we encourage you to apply. For further information about CLS and to register your application please visit: https://learning-systems.org Keywords: optimization Machine Learning Control Systems
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machine learning, computer vision, multimodal perception, probabilistic modeling, and interpretable artificial intelligence. The research will be conducted in collaboration with leading national and
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. Publish findings at the intersection of AI, perception, XR, and universal design. We are looking for a candidate who: has a strong background in machine learning, computational perception, XR, HCI
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learning, particularly with deep learning, perception models, and generative AI. • High proficiency in Python alongside experience using a deep learning framework, with a strong preference for PyTorch
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contextual and behavioral cues to understand human actions, anticipate future behavior, and reason under uncertainty. The methodological scope includes machine learning, computer vision, multimodal
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and machine-learning algorithms for conflict detection, collision avoidance, flight-path planning, and rerouting under uncertain and dynamic maritime traffic conditions. Develop perception- and
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or other ML-based techniques for robot control is considered a plus. You have excellent computer science skills (python, git, linux, ROS, etc.) . You have hands-on experience with machine learning
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, organization, and function; models of perception, cognition, learning, and behavior; and statistical and machine-learning approaches for understanding neural data and brain function. We are particularly
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mapping methods with advanced machine learning feature detection to enable evolving subsea scenes to be robustly aligned across repeat surveys. You will develop probabilistic models to detect subtle
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, self-motivated, and team-oriented person who enjoys working on the theoretical and applied foundations of acoustic machine learning. Your CV comprises: Solid foundations in signal processing and