42 parallel-processing-bioinformatics-"https:" Fellowship positions at UNIVERSITY OF SOUTHAMPTON in United Kingdom
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interests in tumour immunology, T cell engineering, and immunotherapy. You will join a multidisciplinary team spanning immunology, and bioinformatics, with access to advanced flow cytometry, 10X Genomics, in
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in process conditions can introduce defects that degrade device performance, reduce yield, and drive up production costs. Addressing this challenge requires new modelling approaches that can capture
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discipline. You will have demonstratable experience in field-based research, microbiology (field and laboratory), bioinformatics (e.g. metagenomics and high-performance computing), and handling plants and soil
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immunotherapy. You will join a multidisciplinary team spanning immunology, and bioinformatics, with access to advanced flow cytometry, 10X Genomics, in vivo imaging platforms. Key responsibilities To work within
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including anechoic chambers and controlled experimental environments, ensuring high-quality, repeatable data. Develop and apply signal processing workflows for weak transient signals, long-term monitoring
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that applicants may seek flexible working patterns which will be considered as part of the recruitment process. For further information including key benefits designed to help maintain and support employees' well
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robust, reproducible genomic bioinformatics pipelines, using modern workflow systems and high‑performance computing platforms. Produce rigorous, high‑quality research outputs that advance applied genomic
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a new class of active mirror to underpin the next generation of thin-disk lasers. What You’ll Do: innovate: develop scalable RPLD processes to fabricate REDS active mirrors collaborate: work with a
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Science (ECS) (http://ecs.soton.ac.uk ). Ranked in the top 1% of universities globally and among the UK's top 20 for research, the University of Southampton has an international reputation for its research
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may play in stress responses, below-ground processes and pollination. The research combines controlled experiments, advanced measurement and multiphysics modelling, and will generate open datasets and