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Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig | Leipzig, Sachsen | Germany | 3 months ago
microscopy and ultra-high-field imaging Neuroimaging software (SPM, FreeSurfer, hMRI toolbox) Programming in Python and/or MATLAB Multimodal data integration Complementary elemental analysis (e.g., PIXE, XRF
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statistical modeling and analysis of human performance data (reaction time, workload, situation awareness), including mixed-effects/multilevel models. Programming proficiency in Python, R, and/or MATLAB
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. Training is available in a variety of super-resolution quantitative microscopy and image analysis techniques, and mass spectrometry. PI commitment to mentoring and career development opportunities tailored
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settings native speaker competence in a non-European language (preferred) experience in quantitative methods of typological analysis demonstrated ability to conduct research/scholarly activities under
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studies in participatory sustainability governance from all world regions. Data analysis support (WP 3): You will support and advise the four PhD researchers in applying quantitative and qualitative methods
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Perform NGS library preparations, such as whole-genome and whole-exome sequencing, 10x RNA-seq, etc. Perform cell culture procedures, including culture of primary cells, flow cytometry, single cell analysis
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: A WashU-funded study examining patient navigation and human-centered design approaches in cancer care in Kenya. The fellow will support study coordination, data analysis, and dissemination. • PRIME-KS
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analysis of Islamic theological discourses on reform and renewal in a global and European context Experience in acquiring third-party funding for interdisciplinary and application-orientated research
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/ ). Together, CAHBIR, the CCNP, and a broad network of Rutgers-affiliated clinical partners provide an exceptional environment for training in clinically focused computational and cognitive neuroscience research
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The Pluskal lab at IOCB Prague combines cutting-edge experimental (e.g., LC-MS, metabolomics, RNA-seq) and computational (e.g., bioinformatics, molecular networking, machine learning) approaches