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Field
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students and PhD students. Applicants will have, or be close to completing a PhD in a relevant field and possess relevant experience, in the area of probability or statistical machine learning. They will
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of original machine-learning based algorithms and models for multi-modal ultrasound guidance that are intuitive for a non-specialist to use while scanning and trustworthy. You will work with clinical domain
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interest and documented skills and experience in using computer-based tools to analyse, simulate and predict capture performance of active and passive fishing gears. A track record of publishing in peer
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computational costs by orders of magnitude and enabling breakthroughs in drug design and materials science. The position bridges machine learning and molecular science, with opportunities for collaboration
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/Machine Learning (AI-ML) approaches to meeting this challenge. Possible topics include, but are not limited to: storylines for plausible narratives of regional climate change, novel algorithms for rare
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to support research groups associated with Institute faculty, in areas such as: ● Machine Learning and Computer Vision ● Natural Language Processing and Data Science ● Biomedical Informatics and
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Mohadeseh Taheri-Mousavi’s group. The postdoc will develop and conduct advanced machine learning techniques combined with computational research to study the mechanical behavior of welds. Responsibilities
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challenge. This project aims to explore data-driven Artificial Intelligence/Machine Learning (AI-ML) approaches to meeting this challenge. Possible topics include, but are not limited to: storylines
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projects in computer vision research, with a particular emphasis on Spatial Intelligence, 3D Computer Vision, and 3D Generative AI. You should hold a relevant PhD/DPhil (or near completion*) in Computer
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: Statistical signal/image processing, deep learning, machine learning, neuromorphic computing Good communication skills and an appropriate publication record are essential. Solid knowledge of Python and C++ is