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the development and implementation of machine learning (ML), computer vision (CV), large language models (LLMs), and vision-language models (VLM) to automate data extraction and interpretation for productivity
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University explores synergies between nonlinear control theory and physics informed machine learning to provide formal guarantees on performance, safety, and robustness of robotic and learning-enabled systems
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We are seeking a highly creative and motivated Postdoctoral Research Assistant/Associate to join the Machine Learning Group in the Department of Engineering, University of Cambridge, UK. This
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/ . The post offers an exciting opportunity for conducting internationally leading research on the whole spectrum of novel machine learning algorithms and practical medical imaging applications, aiming
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, scientific machine learning, and partial differential equations to create a new approach for data-driven analysis of fluid flows. The successful applicant will have experience in one or more of these subject
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techniques from optimization and control theory, scientific machine learning, and partial differential equations to create a new approach for data-driven analysis of fluid flows. The successful applicant will
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One Research Associate position exists in the data-driven mechanics Laboratory at the Department of Engineering. The role is to set up a machine learning framework to predict the plastic behaviour
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one of the following analysis techniques (multiple preferred): normative modelling, dimensionality reduction techniques, machine learning, deep-learning, state space modelling, advanced statistics