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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 2 months ago
Lidar and the Roscoe upper troposphere/lower stratosphere lidar). Additional projects include the development of machine learning and advanced data processing algorithms, and participation in upcoming
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datasets Implement LLM and Machine Learning algorithm Conduct statistical analysis in SAS or Stata Assistant with other ad hoc tasks Required Education Bachelor’s or Master’s degree in computer science
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on the complementary expertise and recognized excellence of its 22 research teams to contribute to the development of the fundamental aspects of computer science (models, languages, methods, algorithms) and to foster
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and evolved 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
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reliable models and algorithms in these contexts (weakly supervised, semi- or unsupervised learning, domain generalization, active learning, federated learning, privacy preservation, noise and uncertainty
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include scientific programming, data analysis, manuscript preparation, meeting with collaborators, etc. In particular, the Research Scientist hired for this position is expected to develop AI algorithms
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information about the lab check out: https://www.moorelabstanford.com/ . About the role: The role will be in-person with hybrid flexibility and is a perfect opportunity for someone looking for a 1-year, fixed
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algorithms, electric and magnetic fields, ultrasound, optics and targeted radiation, microfluidics, controlled force sensing and actuation and related tools for probing and controlling biomolecular systems
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perform 3D single-particle tracking and establish pipelines to characterise the particle motion using a combination of established tracking algorithms and machine-learning-based approaches. Additionally
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networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large language models Statistical learning theory and complexity analysis