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programming skills (Python; familiarity with deep learning frameworks such as PyTorch or TensorFlow is an advantage); Affinity with probabilistic modelling and spatial data analysis; Interest in the physical
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engineering in a unique way. The Atomic Scale Processing group (Prof. Miika Mattinen) in the Department of Chemistry and Materials Science (CMAT), is now looking for: Doctoral researcher (PhD student) in thin
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implementations and hardware-security countermeasures. Experience with hardware reverse engineering, debugging interfaces, or firmware analysis. Experience with machine learning, deep learning, signal processing
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(Prof. Miika Mattinen) in the Department of Chemistry and Materials Science (CMAT), is now looking for: Doctoral researcher (PhD student) in thin films for electrocatalysis Are you passionate about
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, structure preserving deep learning, stochastic differential equations, generative AI, numerical optimization. Strong programming skills (Python, Julia, Jax). Experience with numerical optimization is also
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advanced analytical approaches, including deep learning and machine learning, to improve disease subtyping and risk prediction. You should have a strong willingness to learn, enjoy tackling challenging
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features, the PhD will first learn an individualised cognitive simulator that models how a person generates expressive responses under emotional, social or conversational contexts. The simulated cognition
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experience in Python and, more specifically, in common deep learning frameworks such as PyTorch and jax, for model training and inference have experience with embedded platforms such as FPGAs or RISC-V
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be a game changer. Deep learning models can learn the mapping between material states and ultrasonic responses from simulation data, delivering quantitative predictions once trained, and remarkably
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centres, we provide an unparalleled learning environment for its 24,000 students and 13,000 staff. At Cambridge, our mission is to contribute to society through world-class education, learning, and research