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annotation of these metabolomes using multistage fragmentation (MSⁿ) data, incorporating novel computational methods and strategies (e.g. spectral matching, network-based approaches, machine learning) where
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machine learning is essential; while structure prediction or materials chemistry experience would be advantageous, it is not a pre-requisite for the role. This post would be ideal for an ambitious and
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lab integrates machine learning and high-throughput biochemistry to study how proteins selectively recognise their substrates, how this process is perturbed in cancer and how it can be hijacked to find
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qualification/experience in a related field of study. The successful applicant will have expertise in statistical modelling, epidemiology or machine learning and possess sufficient specialist knowledge in
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should have experience of working with computational and analytical techniques in the areas of natural language processing (including, among others, topic modelling), computational linguistics, and machine
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Jan. 2026, based in the University of Birmingham UK. This position will use further develop the novel AI/machine-learning (ML) approach in Chen et al. (2022 & 2024, Nature Geoscience ) and apply
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desirable, good quantitative training (e.g. undergraduate maths courses) is essential. Familiarity with and interest in machine learning approaches applied to biological problems is also desirable. Lab and
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desirable, good quantitative training (e.g. undergraduate maths courses) is essential. Familiarity with and interest in machine learning approaches applied to biological problems is also desirable. Lab and
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, written, and oral communication skills in English. Exhibit strong organisational skills and the ability to meet deadlines and complete projects. Have expertise in machine learning and/or programming (highly
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. Ready to be part of our team? Let’s shape the future together! About the team: The Computational Materials Discovery group is looking for a postdoctoral researcher working in the field of machine learning