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Field
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expertise in machine learning and/or Bayesian models is preferred. This position will involve both methodology development and analysis of multi-omic sequencing data, including spatial transcriptomic data
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power engineering. In condition monitoring non-invasive data is analyzed through machine learning algorithms or by statistical methods. The aim of predictive analysis is to use non-invasive methods
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foundation models and agentic systems and demonstrated capability to produce workable solutions from theoretical formulations Substantial publication history in top computer vision/machine learning venues
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experience using Python machine learning and large language models. Experience in machine learning and NLP for automated misinformation detection, social media data scraping and analysis, and human annotation
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modeling, economic evaluation, and surveillance to enhance analytical tools. Activities will focus on gaining hands-on experience and contributing to emergency preparedness and surveillance planning
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and Data Science, Machine Intelligence, Human-Centred Computing, Cybersecurity, and Mathematical Modelling, underpinned by a strong culture of interdisciplinary collaboration and a commitment to
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investigators in various scientific domains, such as physics, chemistry, and biology, conveying machine learning results and collaborating to explore the insights the models have discovered. - Participated in
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or fracture modelling, or equivalent experience Robust experience with computer coding (e.g., Python, C++) Track record of publication, commensurate with career stage, in internationally recognised journals
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, Bayesian and maximum likelihood approaches, spatial statistics and random forests or other machine-learning approaches and be quick to learn new techniques. Enjoyment of analysis of large and spatially
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) the construction of machine or deep learning models to identify patterns of codon usage in yeast genomes, 2) the implementation of tRNA-sequencing across diverse yeasts and conditions, and 3) the construction