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the possibility of extension. For more details on our research and recent publications, see the Geometric Machine Learning Group’s website: https://weber.seas.harvard.edu For questions, please email mweber
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Collaborate in the use of remote sensing and machine learning methods to detect A. longifolia and to monitor the spread and effects of the biological control agent (occasional collaboration). Activity 4
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are template based and all US measurements are auto-populated into templates for increased accuracy and efficiency. Fourteen fellowship-trained, subspecialized expert faculty perform both image interpretation
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, or a related field. Proven experience in machine learning, deep learning, generative AI and data mining. Strong programming skills (e.g., Python, R, MATLAB, or similar). Experience with data
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applications in chemical and pharmaceutical manufacturing; data-driven modelling and machine learning applications in process industries; advanced process control (APC); model predictive control (MPC); digital
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climate will warm and recover in a net-zero future. As part of this project, you will apply machine learning (ML) methods to discover reduced-order models from data and develop GenAI-based techniques
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Computer/Information Sciences Internal Number: A-179059-11 General Description The Johns Hopkins University Data Science and AI (DSAI) Institute welcomes applications for its Postdoctoral Fellowship program
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the production of polymer latexes that involves a complex, heterogeneous polymerization system and leads to polymers with a diverse range of structures. This project looks to use machine learning to better target
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in Spatial Omics and Multi-Modal Data Integration Duties & Responsibilities: Develop computational and machine learning methods for spatial omics data (spatial transcriptomics, spatial proteomics
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Engineering/ Electrical Engineering. 2. Admission Requirements: Bachelor's degree in Computer Engineering, Systems and Information Technologies Engineering, Electrical and Computer Science Engineering, or in a