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: Machine learning/deep learning model development for biomolecular data analyses and prediction Research Area: Data science and computational chemistry Required Skills: A Ph.D. in relevant field within
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) and dynamic spread models for invasive forest and agricultural pests, integrating multi-source ecological data using machine learning and statistical approaches. Develop and validate phenological models
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Professor Arbel’s lab in the context of a grant focused on developing multi-modal causal deep learning models to predict future disability progression and treatment response in Multiple Sclerosis
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Learning Course Description: Machine Learning applications are increasingly utilized to make crucial decisions in many sectors of our economy and society. These include, but are not limited to, healthcare
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organ-on-a-chip (OOC) models, colony picking and bioprinting). The ideal candidate should have strong expertise performing machine learning (ML), computational biology with the capability and/or
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cases. AI Model Development & Engineering: Design and implement machine learning models for specific business challenges. Optimize AI models through lightweight experimentation, rapid evaluation, and
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: Literature review, data collection, input variable selection, preliminary model building for machine learning based streamflow forecasting. Qualifications: BEng; Very strong ability in coding and ML/DL
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, research areas include Operations Research, Information Engineering, Human Factors, and Applied Machine Learning, all of which seek to improve the systems we as humans rely on to navigate our world
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Status Reports to the Principal Investigator (Dr. Leluo Guan). Will work with Post-Doctoral Fellows to establish omics data storage, transfer and management, and develop machine learning models
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: Literature review, data collection, input variable selection, preliminary model building for machine learning based streamflow forecasting. Qualifications: BEng; Very strong ability in coding and ML/DL