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, including water, CO₂ and nitrate electrolysis, with a particular focus on improving experimental workflows and enabling reliable, high-throughput data generation and analysis. Project PID2025-172289OB-I00
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surface metrology. It is relevant to have experience with analysis techniques such as AFM, WLI, contact-angle, XPS, infrared metrology. You will work in the XUV Optics group at the University of Twente
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 5 hours ago
. Description: This opportunity is for projects that will support advancing our understanding of the lunar polar regions through the analysis of topography, imagery, illumination and thermal conditions, and
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to develop, simulate, and critically evaluate mechanistic models rather than relying solely on descriptive data analysis. Scientific programming ability in Python, MATLAB, Julia, C/C++, or a comparable
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preformed fibrils (10%) 3. Seeded amplification of the subcellular fractions (15%) 4. Comparative analysis of the subcellular fractions and seeded amplified fibrils in cell models for seeding activity and
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. Under the supervision of Luke Johnson, PhD and Jerrold Vitek, MD PhD, the associate will work collaboratively within the NMRC on an existing project supporting the collection and analysis of large-scale
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proteomics, advanced image analysis, and computational approaches to investigate molecular and cellular heterogeneity and to integrate spatial molecular information with histopathological and clinical data. We
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and Prevention Group of HKU (stroke.hku.hk) to assist in neuroimaging data acquisition and analysis, write-up of the results, as well as training of students Apply image analysis skills on clinical and
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swimming. The successful candidate will lead quantitative analysis of animal configuration and related flow field topology utilizing graph theory to assess the impact of swimming and environmental conditions
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NSF-EPSRC project “DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks”. The research will involve collaboration with