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design, train and implement ARCA: an AI foundation model for crop microbiomes. You will work at the interface of deep learning, bioinformatics and microbial ecology, using large-scale microbiome and genome
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, geometric deep learning, representation learning, generative models, or multimodal learning; familiarity with scientific data formats, APIs, databases, cloud computing, high-performance computing, Docker
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 23 hours ago
of proficiency in either R or Python in the areas of machine learning, deep learning, statistical analysis, computer vision, and/or graph analysis Experience with data engineering to create data
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foundational methods for integrating single-cell and clinical transcriptomes; and train, fine-tune, and validate deep learning models using multi-omics and imaging data to predict clinical outcomes such as
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, deep learning, statistical software, and analytics tools to analyze big data sets; and strong communication and collaboration skills. This is a nine-month academic-year appointment with two additional
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as a member of the GHER contributing to the EU research project COMEDI in a consortium of 11 leading partners in the field of data assimilation and deep learning. A successful applicant will develop
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language processing that address concrete problems and are both theoretically rigorous and interpretable. The PhD is funded by the ERC CoG PANDORA (Deep Multimodal Learning for Mining and Generation of Arguments
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Engineering, or related field. At least 3 years of relevant experience in computer vision, artificial intelligence, etc. Proficiency in programming languages such as C and Python Proficiency in deep learning
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experience in deep learning model development and frameworks, and integrating open-source or 3rd party APIs and libraries. Applicants are invited to contact Prof. Bonnie Ngai-fong Law at telephone number 2766
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multimodal sensor data (video, LiDAR, BIM). • Assist in the deployment and acceleration of deep learning models (e.g., TensorRT, ONNX) onto edge computing devices on the robot. • Develop interactive web