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staff position within a Research Infrastructure? No Offer Description Implement algorithms for generating and evaluating synthetic data in Medical Imaging (Task T2.3) or Attribute Recognition in Images
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believe software is a systems engineering challenge rather than a coding problem. See website for details of programs: http://www.coe.neu.edu/graduate-school/multidisciplinary Responsibilities: Teach
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oversee and develop algorithms for analyzing ensemble genomics data, single cell genomics data, single cell merFISH and sequential oligopaints imaging data, as well as novel molecular connectomics data
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for the Advancement of Surgery) initiative. The Research Associate will be responsible for developing machine learning algorithms and creating predictive models. The ideal candidate must demonstrate a robust background
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17 Jan 2026 Job Information Organisation/Company CNRS Department Laboratoire d'Informatique, Signaux et Systèmes de Sophia Antipolis Research Field Computer science Mathematics » Algorithms
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processing and advanced algorithms, identifying predictors of adverse outcomes, using biosignals to improve physiologic understanding and leveraging these insights to improve patient care and reduce false
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National Aeronautics and Space Administration (NASA) | New York City, New York | United States | about 10 hours ago
transfer algorithms for NASA GISS‘s general circulation model (GCM) to study radiative interaction and feedbacks between various atmospheric constituents and the climate system. Potential specific topics
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and conferences. Proven experience in design and implementation of deep learning algorithms. Outstanding programming skills in Python. Extensive experience working on one or more of the following areas
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overtime, and believe software is a systems engineering challenge rather than a coding problem. See website for details of programs: http://www.coe.neu.edu/graduate-school/multidisciplinary Responsibilities
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multiphase phenomena. The study will combine theory, algorithm development, and computational modeling, with the goal of advancing scalable hybrid approaches for next-generation fluid simulations. Who we