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machine learning. The position will involve working with different research groups in the Department of Computer Science at the University of Cambridge, UK. In this collaborative project, we will apply
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manipulation techniques for soft objects based on CNNs. Key Responsibilities: Modelling of soft objects. Development of robot vision system for detection and localization of soft objects using machine learning
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these challenges by advancing machine learning (ML)-based and data-intensive methods for the scalable design, processing, and deployment of DPPs. The advertised PhD position focuses on developing ML-based and
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Simon Fraser University | Northern British Columbia Fort Nelson, British Columbia | Canada | 27 minutes ago
the interface of scientific machine learning with PDEs, optimal transport or optimization. Our Scientific Computing, Machine Learning and PDE research group (SciMAP https://www.sfu.ca/math/research/scimap.html
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Join us to explore the mechanics of soft matter through a unique blend of theory, hands-on experiments, and machine learning. Job description Soft matter such as polymers and hydrogels
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about the most recent advances in machine learning and data management in agricultural research. The participant will have the opportunity to collaborate with multiple USDA ARS scientists on using machine
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will involve the development of advanced data analytic and machine learning techniques applied to the processing of sensing signals obtained from advanced fibre-optic communication systems. You will be
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position is on a 9-month service period with an anticipated start date of Sept 16, 2025. This position is aimed at recruiting an exceptional candidate in the field of physics-based Machine Learning
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and small, contribute to a better world. We look forward to receiving your application! Your work assignments We are looking for one PhD student working on generative AI/machine learning, with
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application! Your work assignments We are looking for one PhD student working on generative AI/machine learning, with applications towards materials science. Generative machine learning models have emerged as a