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include: expertise in programming and coding (preferably using Python and C++) and GUI development; expertise in computational mechanics and finite element simulation and modeling; expertise in laboratory
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for Technology, Economics, and Development (CTED) with a primary focus on artificial intelligence, machine learning, mathematical modeling, and computational analysis. The Associate Research Scientist will play a
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or quantitative methods. We are open to a range of methodological backgrounds, including digital trace data analysis, natural language processing, machine learning, experimental design, causal inference, and
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and biophysics, good computer programming skills, and working knowledge of UNIX, phyton, and shell scripting is highly desired. Experience in molecular simulation softwares and analysis tools such as
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
following areas: High-dimensional probability and concentration/functional inequalities Markov processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations
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in molecular biology techniques such as molecular cloning and RNA analysis are expected. The candidate will work in a multidisciplinary environment that includes collaboration with fellow post-doctoral
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and benefits. The successful applicant will contribute to research examining important social science questions through survey analysis and experimental methods. The position will involve supporting
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at NYU Abu Dhabi and will be co-supervised by Dipesh Chaudhury and Justin Blau. Responsibilities: Perform single cell gene expression from mice brain nuclei and single cell data analysis Stereotaxic
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Medicine Foundation AI Models Agentic and robotic workflow management systems Multi-omics and Integrative Data Analysis Population-level Cohort and EHR Analysis Epidemiology Pandemics Biomedical Imaging
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Computational Social Science, with a starting date of September 1, 2026. Some of the possible research topics include: (i) social media analysis, (ii) collaboration and teamwork, (iii) gender inequality, (iv