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Field
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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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Intelligence, Applied Mathematics, Electrical Engineering, or a closely related field. You have demonstrated expertise in machine learning and deep learning, with experience in time series forecasting or related
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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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cohorts with rich clinical data. Works on advanced modeling of post tuberculosis lung disease risk using approaches including generalized organoid models, and linear models and deep learning. Minimum
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You hold a PhD in Computer Science, Artificial Intelligence, Applied Mathematics, Electrical Engineering, or a closely related field. You have demonstrated expertise in machine learning and deep
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in and motivation for genomics or/and biodiversity conservation studies. Strong background in AI/ML fundamentals and extensive experience with deep learning (DL) methods. Demonstrated proficiency in
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Join us at the forefront of life science AI. We are looking for a postdoctoral researcher to develop cutting‑edge, multimodal transformer‑based deep learning methods to extract insight from genomic
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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 23 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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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 7 hours ago
mathematical and computational models of the coupled thermo-fluid dynamics of wind and surface-wave driven shelf-water polynyas in polar oceans. As magnets for biological activity, centers for deep salty water