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of the land ice contribution to sea level rise until 2300 with machine learning. You will develop probabilistic machine learning “emulators” of multiple ice sheet and glacier models, based on large ensembles
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at command line and BASH scripting Experience working with large scale, complex datasets and data wrangling skills Strong publication record and familiarity with the existing literature and research in
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related field together with strong programming skills in Python, R, or similar languages, and proficiency in high-performance computing. You will have experience in large-scale genomic data analysis. You
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challenges of our time, with rising ideological divides and fragmented information ecosystems coinciding with increasing stress, anxiety, and declining well-being. Polarizing online content not only fuels
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amenable to therapeutic targeting. This position will involve the application of advanced data science approaches to explore large-scale clinical datasets extracted from electronic health records, with
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, spanning over 400 boxes, 100 volumes, and a large historical book collection, holds critical documents related to its colonial and financial history. A significant portion will return from the University
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to the 4th February 2026. You will be investigating the safety and security implications of large language model (LLM) agents, particularly those capable of interacting with operating systems and external APIs
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targets. You will take a lead on development of computational approaches to integrate multi-omics data from patient samples, including DNA methylation, histone modifications, single-cell transcriptomics and
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networks. The research will employ mathematical modelling and computer simulation to identify synaptic plasticity rules which enable effective learning in large and deep networks and is consistent with
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defending the cultural value of knowledge for its own sake. You will also possess computational expertise in data mining and / or analysis, ideally including language processing, and be able to work with an