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Project Title: Intrinsically-aligned machine learning In a truly cross-disciplinary effort, this project, funded by the Leverhulme Trust and in collaboration with the University of Manchester, will leverage
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-informed machine learning (PIML) with domain-specific engineering knowledge. By embedding physical laws and corrosion mechanisms into data-driven models, the research will produce more accurate
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Learning Your role and goals Trustworthy & Adversarial Computing Lab (https://taclab.aalto.fi ) led by Sebastian Szyller is looking for a doctoral researcher (PhD student) to pursue a degree in trustworthy
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quantum mechanical effects are typically too expensive for simulations of disordered systems like liquids. This PhD will develop and deploy the tools needed high-fidelity simulations: machine learned
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CDT in Machine Learning Systems About the CDT Machine Learning has a dramatic impact on our daily lives built on the back of improved computer systems. Systems research and ML research are symbiotic
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by detecting and predicting threats such as pests, diseases, and environmental stress in line with the UK Plant Biosecurity Strategy. The project harnesses computer vision, deep learning, and large
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change accelerate, we urgently need smart, evidence-based tools to plan, manage, and protect our marine ecosystems. At the forefront of this innovation is machine learning. Its ability to process complex
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Can We Teach AI to Outsmart Humans in the Werewolf Game—Without Changing the AI Itself? Large Language Models (LLMs) have dazzled us with their ability to converse, code, and create—but they still
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. Project details In this project we aim to develop graph deep learning methods that model spatial-temporal brain dynamics for accurate and interpretable detection of neurodegenerative diseases
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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