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Field
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training algorithms and AI architecture Image reconstruction, segmentation, and classification High performance computing for spatiotemporal data Major Duties/Responsibilities: Develop foundation AI models
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Research Assistant for Applied Mathematics - Responsibilities - Analyze a dataset of case law in the domain of worker classification and develop algorithms to predict judges decisions based
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to design and implement machine learning techniques and algorithms. * Demonstrated expertise in the Linux computing environment. * Excellent planning, organization and execution skills. * Positive attitude
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available single-cell sequencing data generated from patient samples and mouse models, we will enhance and apply machine-learning based algorithms to deconvolute bulk tumor RNA-seq samples to distinct immune
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place technologies and to develop digital twin algorithms to assist clinicians in developing treatment plans. Analyzes complex sensor data, works with a multidisciplinary team to develop health digital
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grid analytics, and scientific imaging. The successful candidate will design and implement sparse algorithms for large-scale scientific and numerical computations. This role offers an exceptional
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, focusing on applications within the healthcare, education, and environment sectors. Designs generative AI techniques and algorithms for data integration and computational models, with objectives to amplify
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The successful candidate will be responsible for developing computational and systems biology approaches to analyze spatial omics data and single-cell omics data (e.g., scRNA-seq, scATAC-seq, single
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genetic knockouts in yeast and mammalian cell lines, and protein purification. Job Responsibilities: 35%: Computational algorithm development and data analysis 35%: Design and conduct experiments with yeast
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, and ionization cross sections using data derived from experiment and computation. - Implementing algorithms to study the performance of AI/ML classification models. - Assessing