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Field
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-of-the-art methods, datasets, and challenges Proven experience with: Video data processing for learning and inference Deep learning architectures for video analysis Python programming and PyTorch framework
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integrates neuroimaging, sleep measurement, digital phenotyping, electronic health record (EHR) data, and deep clinical phenotyping to identify predictors of symptom trajectories and functional outcomes in
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. The successful candidate will pursue an active research agenda and contribute to the Climate Policy Lab?s ongoing projects, as well as contribute deep expertise in either China, Latin America, or system dynamics
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Project Overview The DiSTAP programme addresses deep problems in food production in Singapore and the world by developing a suite of impactful and novel analytical, genetic and biosynthetic
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and inference of unmeasured observables. 2. Improve computational methods to extract the CKM matrix element Vub from inclusive decays using deep learning approaches. Environment: ICCUB is a María de
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expertise in deep learning and representation learning applied to biological data, experience with large-scale multi-omics datasets (such as single-cell and proteomics), and strong programming skills in
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, multimodal foundation models, continuous learning systems, or agentic AI models. Experience with state-of-the-art multimodal foundation models and agentic AI frameworks Experience in large-scale deep learning
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for candidates with a strong computer science background, such as algorithms, machine learning and data science. Key Responsibilities Develop, implement, and evaluate machine learning and deep learning models
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language processing), machine learning, or data science. Preferred Qualifications Expertise in Deep Learning and LLMs(Large Language Models): Knowledge in building applications and fine-tuning models like Llama 3
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 5 hours ago
inventories) with satellite remote sensing data (e.g., spaceborne lidar and/or hyperspectral observations) and apply machine learning and deep learning approaches to address these questions. This position is