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machine learning research software, preferably using Python and PyTorch. An interest in foundation models, self supervised learning, multimodal learning, and 3D perception. An affinity for translating
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also include generative or predictive modeling of dynamic radar scenes. The project combines methodological machine learning research with experiments on real automotive sensor data. You will have access
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, behavioural research, and artificial intelligence to investigate natural sound perception in humans and machines. Your colleagues: You will join the AuditoRy Cognition in Humans and MachInEs (ARCHIE) lab
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demonstrable experience with programming in Python and implementing statistical or machine learning algorithms. You have experience with software development practices such as testing and version control with
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across a range of application areas, including education and healthcare. As these systems are increasingly deployed in high-stakes environments, there is a growing need for machine learning models
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at the intersection of AI, deep learning, computational neuroscience, and vision science. You'll develop biologically realistic neural networks to understand how individual differences in the brain shape perception
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on AI-assisted reverse engineering of integrated circuits for hardware assurance and intelligence analysis. The project is conducted within the Deep Learning for Perception and Data Science, Safety and
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foundation in metascience, yet explicitly strives to bridge and contribute to interdisciplinary research fields in areas as varied as human-computer interaction, decision-making, data science, social and
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scientific initiative focused on AI-assisted reverse engineering of integrated circuits for hardware assurance and intelligence analysis. The project is conducted within the Deep Learning for Perception and
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combines extended reality development with machine learning techniques to improve interaction, perception, environment understanding, data interpretation, or context-aware assistance in constrained