22 algorithm-development-"UCL" Postdoctoral positions at UNIVERSITY OF HELSINKI in Finland
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metagenomics assembly” funded by the Research Council of Finland in the research group of University Lecturer Leena Salmela. We develop models, algorithms and data structures for high throughput sequencing data
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, calibration, and the development of analysis tools and software. Our key focus areas are the physics of jets, top quarks, and EWSB, including the development of novel machine-learning methods for high-energy
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, automated reaction mechanism generators, and high-resolution local scale air quality modelling. The project is funded by the Jane and Aatos Erkko Foundation. Research tasks include Developing future emission
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their effects on ecosystem functioning are responding to ongoing environmental change, the project takes advantage of the unique long-term datasets collected in Finland. REC also develops state-of-the-art
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, the project takes advantage of the unique long-term datasets collected in Finland. REC also develops state-of-the-art methodology for analysing long-term spatially structured data sets within a joint species
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inspiring, creative teamwork in a modern, research-oriented institution in the heart of Helsinki. The researcher will develop their expertise in the field of textual studies of the Hebrew Bible / Old
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participate in a project which investigates and develops novel immunotherapies (cell therapies) to cancer, utilizing both mouse and human systems. Applicants should possess a PhD degree or be close to
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of high-dimensional datasets, and developing bioinformatic pipelines for high-throughput analysis in high-performance computing (HPC) clusters. The work provides the possibility to develop skills in
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the research group of Professor Klaus Nordhausen in the project “Signal recovery in noisy spatial data”. The research group develops modern and efficient multivariate statistical methods tailored
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resistance and microbiomes, statistical analysis of high-dimensional datasets, and developing bioinformatic pipelines for high-throughput analysis in high-performance computing (HPC) clusters. The work