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Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3S) | Portugal | 16 days ago
University Medical Center (LUMC) in the Netherlands. The PhD fellow will be responsible for performing and analyzing Multiplex Immunophenotyping images, as well as detailed flow cytometry assays
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hospital intensive care unit. The post holder will have primary responsibility for the development and evaluation of the system, and of the data processing and image display software. The post holder will
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people with subclinical and clinical disordered eating and related body image concerns and body image management efforts. Appointment at Grade 7 is dependent upon having been awarded a PhD; if this is not
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the excellent opportunity to spend some time with the Behl Group “The Autophagy Lab” . The research will further span cutting-edge imaging technologies, single-cell sequencing, metabolomics, proteomics and
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you will need to evaluate all active thermography approaches for composites inspection, develop a database of raw and processed thermographic images of different defects - geometries on composites, test
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therapies and clinical trials. Your responsibilities will include: processing FASTQ and BAM/CRAM files, and producing variant call files, generating input data for AI tools and implementing secure
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computing. With extensive experience in medical image analysis, computer vision, and AI systems through collaborations with leading institutions. Key Responsibilities: Conduct advanced research in the areas
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at the Science for Life Laboratory in Stockholm, a centre for large-scale life-sciences. NRM has a close collaboration with nearby Stockholm University, which includes joint supervision of PhD students. NRM has
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to apply E-mail pinheiro@ubi.pt Requirements Research FieldTechnology » OtherEducation LevelPhD or equivalent Skills/Qualifications - Hold a PhD degree in Electrical and computer engineering or equivalent
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the group's research on developing novel machine learning/computer vision methodology. The focus of this project will be on the development of deep learning methodology for spatio-temporal medical image