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Field
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. Knowledge, Skills, and Abilities: Advanced in data analysis, modeling, machine learning, and AI, using Python and SQL, with extensive experience in libraries and frameworks such as Pandas, NumPy, scikit-learn
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Center for Drug Evaluation and Research (CDER) | Southern Md Facility, Maryland | United States | 7 days ago
methods in pharmaceutical quality. Potential research topics include: Machine learning and deep learning methods for process monitoring, anomaly detection, fault diagnosis, and prediction of critical
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requirements: Candidates must hold, at the time of application, a Bachelor’s degree in Informatics Engineering or related fields. Candidates must also have knowledge in: i. Deep Learning and LLMs: practical
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We are seeking a Research Fellow - Data Science professional with strong expertise in machine learning, deep learning and high-frequency physiological signal analysis. This is a unique opportunity to
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/ESA_selects_Harmony_as_tenth_Earth_Explorer_mission ) The candidate will develop and apply cutting-edge remote sensing or photogrammetry methods, in combination with data science approaches such as machine learning and data assimilation via
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, Skills, and Abilities: Expert in data analysis, modeling, machine learning, and AI, using Python and SQL, with extensive experience in libraries and frameworks such as Pandas, NumPy, scikit-learn, Gensim
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the shallow subsurface (<10 meters depth). Experience with soil moisture/salinity and sapflow sensors. Experience using neural networks and machine learning tools. Stipend $70,000.00 – $80,000.00 Yearly Point
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Python and deep learning frameworks (e.g., PyTorch) Interest in applied, industry-collaborative research Where to apply Website https://www.timeshighereducation.com/unijobs/listing/413018/research-fellow
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stakeholders, end users, industry partners, and multidisciplinary research teams to support system integration and project delivery. Preferred Skills Python, C++, ROS/ROS2. Computer Vision (OpenCV, Deep Learning
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areas: Computer Vision and Image Processing Machine Learning, Deep Learning, and Reinforcement Learning Large Language Models (LLMs) and Multimodal Models Generative AI, Agentic AI, Physical AI, and