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methods with the ability to implement and evaluate machine-learning systems at scale. Candidates may come from topological data analysis, geometric deep learning, network science, statistical physics
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, machine learning or related areas. Faculty of Science & Engineering Department of Electronics & Computer Engineering Contract Type: Specific Purpose Salary Scale: €55,399 - €60,250 p.a. pro rata University
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checks with advanced machine learning architectures, specifically Long Short-Term Memory (LSTM) networks and Variational Autoencoders (VAEs). The researcher will use historical QC archives dating back
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Applications are invited for the following position: Title of Post: Research Assistant in ADAS Systems JOB SYNOPSIS The successful candidate will undertake the development datasets, machine learning
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machine-learning models to investigate how brain, environment and social factors influence cognition, ageing and neurodegenerative disease. The role will contribute primarily to WP6 and related analytical
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strands. • Bespoke modelling of tumour metabolic function using 3D and 4D imaging data • Cancer patient risk prediction using machine learning (with experience in particular in radiomics and transcriptomics
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research projects. This role requires excellent communication and interpersonal skills, computer literacy, and data management. He/she is a key member of the project team and should be able to prioritise and
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concepts from nonlinear system identification, optimisation, computational complexity, and data-driven modelling, with the long-term objective of extending these ideas to modern AI and machine learning
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Post Doctoral Researcher Rinn Artificial Intelligence – Research & Innovation in Data Science and AI
patient risk prediction using machine learning (with experience in particular in radiomics and transcriptomics) • Multi-omics for non-cancer health screening applications, • Machine learning modelling
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Researcher in The School of Chemistry. The InTeleCat project involves the use of machine learning and AI as applied to organic synthesis. It is a large collaborative project involving researchers in the US