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research, machine learning or artificial intelligence (e.g., large language models, EHR foundation models), causal inference (e.g., target trial emulation), and child health research. The research program
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and enthusiastic individual who meets the following criteria: Recently earned a Ph.D. in bioinformatics, computational biology, computer science, electrical and computer engineering, or a related
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 27 days ago
data analysis methods to study biological memory circuits and their applications to machine learning. Building on recent work from the Fiete Lab, the role focuses on identifying principles of biological
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and social data. Minimum Requirements: A PhD degree is required. Preferred Qualifications: Experience with large-scale or high-dimensional datasets (e.g., cohort, registry, or omics data) Familiarity
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/Resume. Required Education and Experience PhD in Computer Science, Electrical Engineering, Computer Engineering, or a related field. Preferred Qualifications Strong publication record in top-tier AI
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systems, large multimodal foundation model training and/or finetuning, and continuous learning pipelines. Experience in multi-modality data analysis (e.g., image, video, text). Experience working in
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as a one‐year appointment, but renewable annually based on performance. The position involves postdoctoral work in developing efficient methods and tools for analyzing large-scale biomedical data with
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machine learning approaches with large-scale biological data to automate genome curation by detecting, interpreting, and correcting structural errors, reducing manual effort from weeks to minutes thus
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learning PREFERRED QUALIFICATIONS: Experience using computational methods to analyze large-scale high-dimensional biomedical data relating to clinical information, genetics, genomics, radiomics, and/or
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applications for a fully funded postdoctoral associate position. This position, available immediately, focuses on developing machine learning and deep learning methods for analyzing large-scale single-cell DNA