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Field
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for preprocessing, integration, and modeling of heterogeneous data (spatial, temporal, tabular) -Conduct research in explainable AI and uncertainty quantification applied to agronomic decisions. -Collaborate with
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computational power and the increasing availability of large volumes of remote sensing data with finer spatial and temporal resolutions have significantly transformed the way we approach climate and weather
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processing and simulation. Analyze high-throughput photon event data to extract spatial and temporal correlations. Collaborate with experimental staff on algorithm validation and feedback-driven experiment
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. You have a background in machine learning for spatial data (e.g., random forest, neural networks) or are open acquiring these skills. You have experience with handling large geospatial datasets and
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methodology for analysing long-term spatially structured data sets within a joint species distribution modelling framework. For more information on REC, please see https://www2.helsinki.fi/en/researchgroups
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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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The successful candidate will be responsible for developing computational and systems biology approaches to analyze spatial omics data and single-cell omics data (e.g., scRNA-seq, scATAC-seq, single
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using mouse models for neurodevelopmental disorders and brain inflammation. Run spatial omics approach including multiplexed protein imaging and transcriptomics. Collect and analyze data from genome-wide
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for accelerated deployment of new medicines”, is funded by EPSRC and supported by 6 industry partners. The project is developing spatial tissue patterning methodologies to control cellular and matrix
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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