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resilience and its change over time in the past (based on Earth observation data), present and future (based on Earth system model simulations for different future scenarios, e.g. using the CMIP6 ensemble and
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computational models to map co-expression networks and predict systemic disease transitions. Characterise intestinal microbiome changes and their correlation with inflammatory diseases. Computational modelling
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to integrate various structural biology data (NMR, SAXS, FRET, EPR) as well as computational models and simulations to create and interpret conformational ensembles of disordered protein regions, with the goal
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drug and genetic screens (e.g., DepMap, PRISM) Common bioinformatics resources and pipelines (e.g., NCBI, Ensembl, BWA, STAR, GATK, VarScan, DESeq) Proficiency in Linux and programming languages such as
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learning (AI/ML) being a major focus. Many of the laboratory's interests center around the identification of small molecules using mass spectrometry data, and the use of language models to predict
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project RECLESS (Recycling versus loss in the marine nitrogen cycle: controls, feedbacks, and the impact of expanding low oxygen regions). RECLESS aims to predict how ongoing ocean deoxygenation impacts
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Conservation D. How plant cell shape and tissue structure help cells stick together E. DETERMINER - Determining appropriate monitoring frequency for phytoplankton F. Predicting bacterial protein expression
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-function in a steroid-synthesizing enzyme is a predictive biomarker of poor outcomes after hormonal therapy (Hearn, et al.Lancet Oncol . 2016;17:1435-44;JAMA Oncol . 2018;4:558-62;JAMA Oncol . 2020;6(4
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to protein engineering and molecular cloning, Experienced in bioinformatic genome analysis and computational tools related to protein structure analysis and prediction, Sufficient expertise in standard
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health and translational medicine using a "bench-to-bedside" approach. By harmonising and analysing diverse biomedical data, while focusing on the secure data processing and predictive modelling, we aim