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electrophysiology data obtained through collaborations and perform cross-species comparisons. We use machine learning techniques for neural data analysis and computational modelling with a special interest in
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machine learning methods to model changes in the brain over the lifespan, including brain structure and function, and how those changes relate to environment and genomics. What We Offer As an employer, we
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the leadership of Principal Investigator Dr Andrew Siemion. Listen's interdisciplinary research has synergies with many of the department's research priorities, including exoplanet studies, machine learning
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machine learning methods to model changes in the brain over the lifespan, including brain structure and function, and how those changes relate to environment and genomics. About the Role The post is funded
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their surfaces. Machine learning methods are used to close the complexity gap. Currently, the group consists of three full professors, one associate professor, 6 postdocs and about 15 PhD and 7 master
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and data processing skills: experience of programming in one or more languages (e.g. R, C/C++, Python, Matlab). Practical experience of algorithm development and implementation of machine learning
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, including exoplanet studies, machine learning, cutting-edge radio instrumentation and digital signal processing, citizen science, sky surveys, and studies of transient and variable objects. Listen is deeply
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Postdoctoral Researcher in ML for Dynamical Systems Representation, Prediction, and State-estimation
for predictive modelling and state estimation for fundamental applications within physical sciences. Your role The main research responsibilities involve building cutting edge machine learning techniques
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” research themes. The successful candidate will have: a PhD in Translation Studies/Machine Translation; practical experience conducting data-driven research in a machine translation/large language models (LLM
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and machine learning. Topics of interest in this area include, but are not limited to: natural language processing, large language models, graph learning, prompt engineering, knowledge graphs, knowledge