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sciences, computer science, machine learning, and education research. Research Themes The research themes identified for the NTO postdoc include, but are not limited to, the following: Developing
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to) the qualifications of the selected candidate, budget availability, and internal equity. Pay Range: $86,100 Aligning Machine Learning Models with Algorithmic Reasoning Tasks We are seeking a postdoctoral researcher to
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spanning multiple diseases. About the lab: The Glastonbury Lab is focused on developing and applying Machine Learning to problems in digital pathology and spatial transcriptomics. The group has a particular
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will work on multiple projects funded by NIH/NHGRI. The objective of the position is to develop novel statistical methods and computer software and analyze large scale biological data from biobanks
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transformers, Machine Learning, Power systems. Expertise with the following engineering tools and programming languages can be an advantage but not limited to: MATLAB, PLECS, PSCAD, others
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data from both tissue and single cells, for improved understanding of Alzheimer progression. Experience in brain disorders, machine learning and deep learning will be a plus. Interested candidates should
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longitudinal modeling, machine learning methods, subgroup analysis, or other advanced modeling techniques is highly desirable. Software Proficiency: Experience with neuroimaging tools such as AFNI, SPM, FSL
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because of the large parameter space and interdependence of different variables that affect the desired performance. Artificial intelligence and machine learning models have demonstrated the potential
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. The successful candidates can choose to work on one of the following areas with Yuansheng Cao and collaborate with multiple researchers across the world: 1. nonequilibrium thermodynamics of biological information
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
models, and their coupling, using machine learning. The postdoc will be expected to collaborate with other postdocs at Princeton and with other members of the M2LInES project across multiple institutions