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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 19 days ago
include advancing foliar trait retrieval algorithms, atmospheric correction over dense humid tropical canopies, machine learning and dimensionality reduction for handling big data, landscape evolution
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of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI implementation. Ability to apply AI to interdisciplinary research or developing AI models
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on large-scale public and newly generated datasets. As Postdoctoral Researcher in AI, you will take a leading technical role in developing ARCA. You will explore which model architectures and learning
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their added value against simpler machine-learning baselines; train and evaluate ARCA on large-scale microbiome datasets, with attention to sparsity, batch effects, scalability, generalisation across studies
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Research Assistant or Postdoctoral Research Associate About the Role The School of Electronic Engineering and Computer Science at Queen Mary University of London seeks to recruit a Research
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control, network optimization, or joint communication and sensing is highly desirable. Experience with artificial intelligence, machine learning, reinforcement learning, or optimization techniques applied
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checks with advanced machine learning architectures, specifically Long Short-Term Memory (LSTM) networks and Variational Autoencoders (VAEs). The researcher will use historical QC archives dating back
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Job Description Apply now Job Title: Postdoctoral Associate - Cell and Gene Therapy Division: Pediatrics Work Arrangement: Onsite only Location: Houston, TX Salary Range: $63,480 - $64,356 FLSA
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impact? We are currently seeking a/an University Assistant postdoctoral 49 Faculty of Social Sciences Startdate: 01.11.2026 | Working hours: 40 | Collective bargaining agreement: §48 VwGr. B1 lit. b
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, design and analysis of virus-derived RNA libraries, and development of machine learning models for detecting functional elements in viral metagenomic datasets. This project is a collaboration with the