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computational pipelines for multiplex imaging, spatial transcriptomics, single cell RNAseq, and multi-omics data integration. Lead graph-based network and machine learning analyses of tumor immune
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scientific leaders and researchers. Job responsibilities The project aims to advance the use of machine learning techniques to model and understand plasma turbulence in magnetically confined fusion plasmas
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to revolutionize agriculture in Morocco by combining cutting-edge technologies, including crop growth models, remote sensing data, data assimilation, machine learning, and seasonal weather forecasts. As a
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Oncology Department at City of Hope is the largest Radiation Oncology enterprise in the southern California with more than 25 Linear Accelerators (Linacs) including special designed machines such as MR
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analytical backbone of the programme. It develops sensor-enabled diagnostic cells, multi-modal data pipelines and hybrid physics-informed machine learning approaches to understand interfacial behaviour during
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aims to improve electrodialysis (ED) for REE separation by developing advanced membranes and integrating AI-driven optimization techniques. By combining materials innovation with machine learning
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machine learning and NLP methods including rule-based, supervised and unsupervised learning, ANN and deep learning, large language models. Ability to self-learn new and cutting edge research areas in NLP
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assist the project leader in the research project – “Smart Holistic Insurance Enhancement using Learning Algorithms & Dialog Data (S.H.I.E.L.D.)”. They will be required to: (a) develop machine learning
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conditions. The researcher will also work with team members within the consortium in generating necessary data required for developing a machine learning model for storm surge prediction. Key Responsibilities
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, spatial transcriptomics, single cell RNAseq, and multi-omics data integration. Lead graph-based network and machine learning analyses of tumor immune microenvironment architecture. Collaborate with wet lab