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literature search and preregistered meta-analysis Coordinate MRI data collection with the technical team at the Leiden University Medical Center (LUMC) Perform fMRI preprocessing and statistical analyses
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project that links processing speed measures to previously acquired functional and structural MRI data. The project will also involve simultaneously acquired and co-registered fMRI and EEG measurements
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. You will develop methods to integrate diverse medical imaging modalities (e.g., X-rays, MRI, Ultrasound) with Electronic Health Record (EHR) data across heterogeneous cohorts and healthcare systems
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flow cytometry, confocal microscopy, MRI/PET imaging and bioinformatics tools. • Work with team members across different disciplines such as chemistry, biology, neurosurgery and engineering
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connectivity analysis and structural MRI data analysis. Strong background in neuroscience and psychology. Experience working with clinical datasets, neuropsychological assessments, and pathology-related
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developmental research protocols for pediatric populations with neurodevelopmental conditions Facilitating practice and behavioral preparation for MRI scans with participants, as well as with MRI data collection
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-based reconstruction algorithms and image analysis pipelines. These approaches will provide highly efficient high-dimensional MRI image acquisition and reconstruction. We seek an enthusiastic and
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multimodal MRI data from children with NF1 using the Siemens Cima X scanner at Stanford’s Lucas Center for Imaging. The position sits at the intersection of advanced neuroimaging methods, longitudinal clinical
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collaboration in international multidisciplinary teams. Where to apply E-mail seleccion@nebrija.es Requirements Research FieldBiological sciencesEducation LevelPhD or equivalent Skills/Qualifications PhD in
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by The Kempe Foundations. Project description Machine learning and artificial intelligence have had a major impact on medical image analysis in recent years. While CT and MRI provide highly