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(linking phenotypes, imaging, cytometry, or other readouts to transcriptomics) Statistics / machine learning for biological inference (model validation, differential state testing, embeddings/classifiers
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by integrating sparse sampling strategies, neural network–based reconstruction, and a virtual imaging platform. The goal is to develop fast, robust, and clinically viable quantitative MRI methods
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in vivo genetic mouse models, advanced live and intravital imaging, engineered microchip models, primary cell co-culture systems and novel microscopy and analysis methods. The research will provide
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in vivo genetic mouse models, advanced live and intravital imaging, engineered microchip models, primary cell co-culture systems and novel microscopy and analysis methods. The research will provide
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of this unusual Brain-Computer Interface. More information: (7) BRAINET: Overview | LinkedIn The doctoral candidate at Tampere University will develop tools and methods to assess the effects of the non-invasive
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artificial intelligence/geospatial AI, methods of machine learning and deep learning development of computer vision applications and image recognition methods analysis and production of big data, including
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resolution by integrating plasmonic nanopores with a high-speed Raman detection system, an automated control system, computer simulations, and advanced Raman-based bioinformatics. The RamanProSeq consortium
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-in-a-nutshell . The requirements for pursuing a doctoral degree at the University of Helsinki can be found at https://www.helsinki.fi/en/research/doctoral-education/eligibility-and-educational
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of glioblastoma will be extended to animal experiments, in which, for example, surgical operations and imaging will be applied. The main responsibilities of the researcher will include laboratory work, data
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lineage cells and CNS health in aging and repair, using advanced in vivo and in vitro models combined with cutting-edge molecular, imaging, and transcriptomic approaches. About the Role The doctoral