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Field
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work environment. We are seeking a part-time Research Assistant in computer vision and machine learning for human behavior analysis and modeling. The successful candidate will investigate new algorithms
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algorithms; customize models for industry and healthcare partners; drive open-source software efforts; contribute to grant proposals, academic papers, and reports; and ensure seamless integration of AI
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workflows for secure, scalable systems to create consistent and reliable software. This position also features a focus on hardware implementation of data exploitation algorithms on low size, weight and power
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high-dimensional, dynamic, networked system, applying techniques from machine learning, causal inference, statistics, and algorithms. No prior biomedical training is required—just strong quantitative
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maintain algorithms for accurate detection, segmentation, and classification of cells and structures in biomedical microscopy images. This position will also work to align high dimensional (HD) images of
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machine learning. Essential Duties and Responsibilities: Develop and implement advanced reconstruction algorithms for correlated and low-dose imaging modalities. Maintain and extend Python-based software
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, Algorithms Appl Deadline: 2025/12/04 8:00PM (listed until 2025/12/04) Fellowship Description: Apply Fellowship Description The IBS Discrete Mathematics Group (DIMAG) in Daejeon, Korea invites applications
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field (e.g. statistics, computer science, or quantitative biology). Experience in the application and development of computational methods/tools or machine learning algorithms. Good computer programming
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environmental engineering (candidate status) and a demonstrated record as a lead author on publication(s) using machine learning algorithms to forecast water quality. Fluency with open source coding languages (R
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: Artificial Intelligence, including but not limited to Machine Learning, Responsible AI, and Computer Vision; Theoretical Computer Science, including Algorithms, Algorithmic Fairness, Game Theory, and Learning