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, or a related field. Solid research background and practical experience in one or more of the following areas: Reinforcement Learning / Deep Reinforcement Learning Fine-tuning and Application of Large
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TAMU. Experience with machine learning and deep neural network techniques. Experience with wearable and sensors placed in the environment. Knowledge of characteristics associated with mild cognitive
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knowledge of methodologies such as deep and statistical learning. Informal enquiries may be addressed to Prof. Andrea Vedaldi (email:andrea.vedaldi@eng.ox.ac.uk) For more information about working at
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intelligence modelling methods and deep learning techniques can also be used in both areas. Your qualifications: Master's degree/diploma/doctorate in computer science, mathematics, physics, engineering, or a
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Post-doctoral Fellow/Senior Research Assistant in the Centre for Information Technology in Education
should possess a doctoral degree in Educational Measurement, Psychometrics, Quantitative Psychology, Statistics, or a related field. They should have a deep familiarity with modern test theories, and a
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cutting-edge technologies — from single-cell multi-omics and deep learning to live-cell imaging and stem-cell–based organoid systems — to predict, observe, and manipulate epigenetic processes. Our lab and
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on the development of deep learning methods for reconstruction and physics analysis of the ATLAS experiment data. The successful candidate will develop innovative analysis methods for the reconstruction or the physics
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marked by ambition and a deep, practical engagement with challenges facing society. We continue to produce versatile alumni and draw faculty and staff eager to be a part of the university's creative
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University of Southern California (USC) | Los Angeles, California | United States | about 1 month ago
, Statistics, or related. Strong skills in machine learning and deep learning, with a fundamental understanding of LLMs. Proficiency inPython programmingand major ML/DL frameworks (e.g., PyTorch, TensorFlow
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Python and GEE for geospatial analysis and data science. Experience with machine learning/deep learning frameworks (e.g., PyTorch, TensorFlow) applied to image or geospatial data. A track record of first