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, computational physics, or a related field; • Strong background in machine learning, probability, and statistics; • Proficiency in scientific programming with Python and deep-learning frameworks such as PyTorch
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to investigate mosaic aneuploidy. Mandatory requirements: Full-time dedication to the project and compliance with the current FAPESP rules for Post-Doctoral Fellowships (https://fapesp.br/en/postdoc ). PhD
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background and research-oriented master thesis in a related field (e.g., signal processing, statistical machine learning, applied mathematics); Significant experience with programming (preferably Python). Good
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healthcare, The key objective is to spearheadresearch and development to revolutionise patient care and inform public health policies. For more details, please view https://www.ntu.edu.sg/c-aim We are seeking
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comfort at the command line, with scripting in Python, R, and/or bash. Familiarity with bioinformatics pipelines and amplicon/metabarcoding or metagenomic data analysis is a strong plus. A willingness to
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in live-cell or live-tissue imaging. Experience of quantitative image analysis and analysing biological datasets using computational tools such as Fiji, Python, MATLAB or similar. The ability to think
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, visualization, or programming tools (e.g., Excel, Python, R, SQL, Tableau, or similar technologies). Demonstrated ability to translate data and analysis into clear, actionable insights for decision-makers
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Workbench, fMRIPrep for existing functional MRI analysis. He/she will design, implement and program functional MRI analysis pipelines using shell scripting, MATLAB, and Python and will use advanced
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | about 1 month ago
methods. Strong programming skills, preferably in Python, and experience working with scientific data. Strong interest in using computational methods to discover biological or medical insights from imaging
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statistical analyses using STATA, R, or Python to extract meaningful insights from large datasets. • Data Management: Collect, manage, and clean primary and secondary data for research projects. • Research