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2025. Encouraged by the continuing success of modern machine learning (ML) techniques, researchers have become ambitious to develop ML solutions for challenging science and engineering problems with
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optimization of multi-modal LLMs. Investigate and implement methodologies to ensure AI authenticity, accountability, and the integrity of digital content. Develop and refine machine learning and deep learning
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approaches, including Low Impact Development (LID) practices (e.g., green roofs, rain gardens), with a specific focus on urban catchments. The research will place a strong emphasis on machine learning
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of machine learning approaches, and uncertainty quantification in ML algorithms. The ATLAS group has four staff members, in addition to two PhD students and one postdoctoral researcher. For this project we
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incorporating adequate prior domain knowledge. Who are we looking for? We are looking for candidates with a PhD degree and demonstrable experience in the field of machine learning with a focus on representation
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This PhD position is part of the WASP-WISE NEST project RAM³ – a multidisciplinary research effort at the intersection of machine learning and materials science. The project brings together PhD
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market trends, incorporating factors such as weather patterns, consumption behavior, and regulatory changes. By leveraging advanced statistical and machine learning techniques, the role aims to provide
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independent higher education provider, offering flexible and inclusive learning across multiple London campuses. We are student focused, digitally forward, and committed to academic excellence reflected in our
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projects and technical leadership. Basic Qualifications: Master's degree or PhD in Computer Science, Artificial Intelligence, Machine Learning, Statistics, or a related technical field. Proven experience in
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position is embedded in a vibrant research environment that includes several PhD students and postdoctoral researchers. The project is a close collaboration between the Computer Vision Group at Chalmers