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. Demonstrated experience in machine learning–based image analysis / computer vision, preferably using microscopy data Strong programming skills in Python Additional background in AI and machine learning
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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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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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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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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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imaging (MRI). The position is within the Research Council of Finland (RCF) consortium project focusing on the development of low-field MRI hardware, sequences, image reconstruction and applications, in
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techniques such as mouse models, in vivo immune cell functional assays, flow cytometry, cell biology, metabolic assays, imaging and omics-techniques (next generation sequencing). Applicants should possess a
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microelectronic hardware. Preclinical in-vitro and in-vivo testing of devices and methods will allow real world validation of this unusual Brain-Computer Interface. More information: (7) BRAINET: Overview