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of autonomous marine vehicle systems for marine survey. We also explore tools for the visualisation, clustering and classification of extensive marine-based image archives, characterisation of change
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mapping, graph theory, hierarchical clustering, etc.) to clinical brain imaging data. This position provides an excellent opportunity to contribute to research that bridges systems neuroscience, pain
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the recurrent discourses running through this corpus, using semantic analysis methods (discourse analysis, graph-based methods, clustering, topic modeling, named entity recognition) combined with
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-supervised and parameter-efficient fine-tuning, compare complementary foundation models and evaluate their representations through retrieval, clustering, classification and external validation. The
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about the innovative work here: https://wyss.harvard.edu/technology/inode-implantable-lymphoid-organs-for-ovarian-cancer-therapy/ What you’ll do: Integrate multi-omics, spatial pathology, and clinical
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about the innovative work here: https://wyss.harvard.edu/technology/inode-implantable-lymphoid-organs-for-ovarian-cancer-therapy/ What you’ll do: Integrate multi-omics, spatial pathology, and clinical
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, multi-omic and spatial transcriptomic/imaging datasets. Develop, benchmark and maintain reproducible analysis workflows in R and/or Python for quality control, normalization, integration, clustering
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, Raman, FTIR in reflection mode) to enable multimodal data fusion and automated material characterization. • Apply and further develop machine-learning and statistical models (e.g. PCA, SAM, clustering
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projects. As part of the Innovative Health Initiative (IHI) project AF-B-STEP (Quantifying Atrial Fibrillation Burden to inform Screening, Treatment, and hEalth Policy, https://afbstep.eu/) , we aim
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preprocessing • Integrate multi-sample spatial datasets to enable scalable normalization, clustering, and cross-sample comparison • Develop protocol-specific systems and documents including process flows