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Field
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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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, you will: Acquire and analyse human neuroimaging data, with a primary focus on high-field fMRI of natural sound perception. Develop and apply AI/NeuroAI models, including deep neural networks, to model
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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. We are working
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discipline. Desirable Experience in machine learning, deep learning, data analysis, numerical modelling, or scientific programming (such as Python, MATLAB, or R) is desirable. Knowledge of hydrodynamic
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Science, Biostatistics or a closely related discipline; have demonstrable experience with training machine and deep learning models, preferably using Python; have a basic understanding of biology and/or
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discipline. Essential Good programming skills, preferably in Python/C#. Experience with machine learning, deep learning, or experimental AI evaluation. Interest in secure distributed AI, federated learning
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experimentation and training. Science of Deep Learning: Exploring mechanistic interpretability and understanding the fundamental drivers of model performance at scale. As an early member of this fast-growing team
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. This project proposes the development of a new CFD simulator for offshore renewable energy applications based on physics-informed deep learning that offers greater efficiency and robustness. This is a unique and
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experimentation and training. Science of Deep Learning: Exploring mechanistic interpretability and understanding the fundamental drivers of model performance at scale. As an early member of this fast-growing team
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related field at the level of a master degree Programming skills (Python) and experience with common machine learning platforms Experience with deep learning, computer vision, medical image analysis