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Supervisory Team: Prof Middleton, Prof Gandhi PhD Supervisor: Matt Middleton Project description: We know of only 20 or so black holes in our galaxy yet predict there should be 10s of millions
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research using big data. He/she will be expected to take a leading role in overseeing research projects and supervising junior research staff. Enquiries about the duties of the post should be sent to Prof
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methods for causal inference in observational data, is strongly preferred. Using various existing large datasets with rich information for knowledge synthetisation and triangulation over the course of the
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university Heidelberg (Prof. Dr. Skyler Degenkolb) seek to bring quantum sensing methods into precision neutron science, further extending the power and reach of these measurements. Innovative new devices can
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ecosystems. They will combine their own field observations in the European Alps and the Arctic with a novel microclimatic dataset and a large Europe-wide database comprising re-surveys of historical vegetation
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. The student will perform ‘big data’ analysis of patient cohorts including time-based evaluation of the impact of introducing CT-FFR as a national health intervention into a healthcare system. Exploratory
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Leibniz-Institute for Plant Genetics and Crop Plant Research | Neu Seeland, Brandenburg | Germany | 7 days ago
architecture of important crop traits like grain yield heterosis. In the era of large population size and dense genomic data such as whole-genome sequencing, new algorithms are needed to remove the bottleneck
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proteins in the mixture together define the key properties of these systems. Predicting these properties by only studying their components might seem impossible... but that is what we aim to do in the Big
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afraid of combining neurobiology and chemistry. You have good statistical skills and experience with analyzing big data (e.g. RNA-seq, spatial transcriptomics). You like to work in a diverse setting and
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, including: Genomic technologies – hands-on experience in long-read sequencing and variant interpretation Bioinformatics – pipeline development, visualisation, and statistical modelling PRS – applying big data