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systems, ML model training, and model deployment, and demonstrated record of advising non-technical stakeholders on technical topics. Excellent communications skills, previous experience of contributing
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of fungal pathogens. The successful candidate will make extensive use of a range of in vivo models of fungal infection and will have access to the wide range of transgenic tools and in vivo immunology
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leveraged to accelerate learning from both classical and quantum data. The project will develop rigorous theoretical frameworks to understand key properties of quantum machine learning models—expressivity
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datasets. Experience in adapting and applying machine learning models to clinical data. Strong ability to communicate scientific work both internally within the research environment and externally to a
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to employ innovative bioinformatics approaches to dissect in both human-relevant models of lung cancer as well as unique cohorts of human lung tissues the tumor "ecosystem" and profile in-depth phenotypic and
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the presence or absence of RNA co-factors. These findings will be validated in cellular and viral models, and a proof-of-principle study will assess the potential of targeting 5′UTR RNA structures
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systems, ML model training, and model deployment, and demonstrated record of advising non-technical stakeholders on technical topics. Excellent communications skills, previous experience of contributing
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models or inflammatory bowel disease (IBD). Familiarity with metabolomics (LC-MS, GC-MS) and analysis of microbial metabolites. Expertise in interkingdom microbiome research (fungi, viruses, archaea
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“greenhouse” (warmer than present) conditions. In i2B we will retrieve new, key Arctic geological archives of past warmth and employ climate models to bring our current knowledge about a warm Arctic beyond the
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; • Proficiency in programming; • Proficiency in additive manufacturing-related software, including 3D modelling and path planning; • Ability to integrate sensors (e.g. laser scanning, thermal imaging) for