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We are seeking a motivated and skilled Research Fellow to join the AUT Regenerative Environments Network (RegEN), based within the School of Science. This role offers an exciting opportunity
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-based tool to perform “horizon scanning” around Net+ Centre research themes: automatically collating news articles and peer-reviewed papers; using large language models and other machine learning
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-dysfunction associated steatotic liver diseases). Our group is part of a vibrant scientific community at the TU Dresden and works closely with national and international partners within initiatives such as the
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-recapture for demography and social network structure; genome re-sequencing for pedigree information and genomic prediction; diagnostic testing for infection status and prevalence of bTB. The Research Fellow
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Large Language Models Natural Language Processing (NLP) Deep Reinforcement Learning Theoretical Foundations of Deep Learning and AI Optimization Techniques in Machine Learning Collaboration and Mentorship
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connectivity of the broader community, training, networking, as well as state-of-the-art research. This post will develop artificial intelligence methods for the prediction of crystal structure, the critical
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ongoing development of communication updates for web, social media and networking Promote equality and value diversity, acting as a role model and fostering an inclusive working culture Support the work
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connectivity of the broader community, training, networking, as well as state-of-the-art research. This post will develop artificial intelligence methods for the prediction of crystal structure, the critical
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-depth characterisation and annotation of these metabolomes using multistage fragmentation (MSⁿ) data, incorporating novel computational methods and strategies (e.g. spectral matching, network-based
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possible. We strive to ensure the diversity of our candidates, and we reflect this ethos in the way we promote the ICRF programme and in our recruitment and selection process. Imperial’s diversity networks