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Field
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techniques and genomic data analysis. Background in and motivation for genomics or/and biodiversity conservation studies. Strong background in AI/ML fundamentals and extensive experience with deep learning (DL
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, deep learning, generative AI, multimodal AI, and LLM. Experience in the integration of artificial intelligence and decision-making. Strong publication record in top-tier conferences and journals
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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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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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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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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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of the following areas: Wireless and satellite communications AI/ML for dynamic networks including Graph Neural Networks, Transfer Learning, Deep Reinforcement Learning, and Transformer-based models
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 11 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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. 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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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