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                We are seeking to appoint a Postdoctoral Researcher for a three-year position in machine learning emulators of ice-ocean processes. The role is part of PRECISE: Prediction of Climate Change and 
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                The Atmospheric Chemistry Research Group (ACRG) and School of Engineering Mathematics at the University of Bristol have developed GATES, a graph neural network (GNN) machine learning model that can 
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                acquired from the field survey Develop machine learning models for prediction and recommendation Job Requirements: Preferably PhD in Computer Science or related field. Expertise in computer programming 
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                The role The Atmospheric Chemistry Research Group (ACRG) and School of Engineering Mathematics at the University of Bristol have developed GATES, a graph neural network (GNN) machine learning model 
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                nonprofits to use machine learning and data science to modernize the public sector. We are an interdisciplinary team of data scientists, social scientists, engineers, and lawyers who are passionate about 
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                related to staff position within a Research Infrastructure? No Offer Description We are seeking to appoint a Postdoctoral Researcher for a three-year position in machine learning emulators of ice-ocean 
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                , and formulation of clinical study design, image processing, machine learning, and statistical analyses to illuminate specific research questions. Among the machine learning techniques, deep learning 
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                - “Towards digital biomanufacturing – developing physics-informed machine learning framework for the advanced multi-modular 3D bioprinting system”. Qualifications Applicants should have: (a) a doctoral 
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                to migration-related diversity. The position contributes to advancing methodological innovation through the creative and reliable use of machine learning, AI, and other algorithmic techniques in qualitative 
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                equivalent. Strong background in machine learning and computer vision. Prior experience in data-efficient classification, synthesis, and detection is preferable. Strong publication records in top-tier machine