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of the ERC Consolidator project AUTOMATIX (see details below), we are seeking a PhD candidate to develop machine learning approaches for constitutive modeling. Context With the advent of machine-learning (ML
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We use cookies which are necessary to make our site work. We may also use additional cookies to analyse, improve and personalise our content and your digital experience. For more information, see our Cookie Policy and the list of Google Ad-Tech Vendors . You may choose not to allow some types of...
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research directions include: Develop a novel microenvironment classification algorithm by extending existing methods Model indoor air quality levels in London microenvironments by combining machine learning
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. Required knowledge Strong background in machine/deep learning, computer vision, or applied statistics. Solid programming skills in Python and experience with deep learning frameworks (e.g., PyTorch
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the DFG Priority Programme “Molecular Machine Learning” and embedded in the research project “Multi-fidelity, active learning strategies for exciton transfer in cryptophyte antenna complexes”. The PhD
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with rare and critical conditions due to a lack of physical grounding. This PhD project aims to enhance traditional machine learning methods by integrating physics, empirical rules, heterogeneous
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] Subject Area: Machine Learning Appl Deadline: none (posted 2025/10/06 05:00 AM) Position Description: Apply Position Description Department: Zaven and Sonia Akian College of Science and Engineering (CSE
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Machine learning has recently made significant progress for medical imaging applications including image segmentation, enhancement, and reconstruction. Funded as an Australian Research Council
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This Masters or PhD project aims to explain the uncertainty of Machine Learning (ML) predictions. To this effect, we must quantify uncertainty, devise algorithms that explain ML predictions and
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Artificial intelligence and machine learning methods for model discovery in the social sciences School of Electrical and Electronic Engineering PhD Research Project Self Funded Prof Robin Purshouse