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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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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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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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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 3 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
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to apply. We seek candidates with expertise in some or all the following areas: density functional theory, deep learning, high-throughput simulations, molecular dynamics, and materials chemistry. Strong
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landscape constrains or enables discovery. The project draws on tools from topological data analysis (e.g., persistent homology, Euler characteristic curves, discrete curvature), machine learning (e.g