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libraries (e.g. NumPy, pandas). Experience with biodiversity-related indicators (e.g. species richness, abundance-based indicators, habitat metrics). Experience working with large environmental datasets, data
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(circRNAs) are associated with Alzheimer’s disease status and when measuring in blood can be used as disease biomarkers. We have generated brain and blood transcriptomic data in thousands of samples with
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: Candidates will need to have completed their PhD by the start of employment. This Postdoctoral Research Associate position will require approximately 100% effort dedicated to research activities, with
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engineering. We seek candidates with an interest in participating in large multi-PI grants and with collaborative research experiences, experience writing proposals for user-facilities, and/or experience
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transcriptional mechanisms, functional genomics, or computational genomics. Strong preference will be given to applicants who have experience analyzing large-scale sequencing datasets, especially MPRA, population
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experiments, ex. generate/characterize Arabidopsis mutants and transgenic plants, large-scale DNA/RNA/protein-extraction, PCR, qRT-PCR, western blot, RNA-seq/Ribo-seq library constructions and analysis, and
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working with large or complex datasets, ensuring accuracy, reproducibility and secure data management. They will be able to work both independently and collaboratively as part of an interdisciplinary team
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project. The project is part of a large collaborative research effort involving Imperial College London, University of Bristol and three other groups at Cambridge. More information about the overarching
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of Research ExperienceNone Additional Information Eligibility criteria The candidate must hold a PhD in physics or engineering sciences. He/she should have an experimental profile and demonstrate a strong
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are twofold. The first goal is to accelerate the solution of the large mixed-integer optimisation problems required to balance energy. The second goal is to develop methods that handle the increasing