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Details Title Postdoctoral Fellow in Deep Learning Theory and/or Theoretical Neuroscience School Harvard John A. Paulson School of Engineering and Applied Sciences Department/Area Position
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related to heart failure and cardiovascular biology. Develop and apply machine-learning and deep-learning approaches to identify disease-associated cardiomyocyte subtypes, cellular trajectories, and
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foundational methods for integrating single-cell and clinical transcriptomes; and train, fine-tune, and validate deep learning models using multi-omics and imaging data to predict clinical outcomes such as
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minimum qualifications at the time of hire. PhD in computer science, data science, or related discipline Track record of publications in Artificial Intelligence and Deep Learning in peer-reviewed
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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
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) years in Computer Science, Data Science, Computational Engineering, Bioinformatics, Computational Social Science, or another highly quantitative field with substantial emphasis on machine learning, deep
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aerial vehicle (UAV) imagery collection and processing, deep learning methods, and rangeland vegetation communities in Oregon and Idaho as part of an interdisciplinary team including researchers in plant
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conduct research related to creating or testing deep learning models for genomics, exploring new techniques related to spatial simulations, or other topics discussed with the PI. Basic Qualifications Core
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scientific "data detective," applying deep knowledge of PV materials and degradation mechanisms to reconcile conflicting reports, validate the data set against established degradation science, identify gaps in
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research. Key Responsibilities: Develop, implement, and optimize machine learning/deep learning models for digital pathology image analysis Analyze large-scale histopathology, omics, and clinical datasets