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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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; distributionally robust optimization; 2) Graph Neural Networks, Large Language Models (LLMs), and geometric deep learning; and 3) federated learning and privacy preserving computing. Basic Qualifications Candidates
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leader • Excellent written and oral communication skills Preferred Qualifications • Background in antisemitism studies • Experience with R, NLP and deep learning libraries • High performance computing
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populations and biobanks for risk prediction, genetic discovery, and genomic medicine. Federated and transfer learning for distributed and privacy-preserving data integration. AI and Deep learning approaches
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required. Substantial experience in machine learning, Python and R programming, and familiarity with deep learning packages (e.g., TensorFlow, Keras, or PyTorch) are essential. Additional Qualifications
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the start date Strong background in computational linguistics or deep learning Demonstrated interest in at least one of: language learning/acquisition, interpretability/mechanistic analysis, human-like
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required) Experience with machine learning / deep learning (PyTorch; model training; GPU workflows). Experience with Transformers / text embeddings / multimodal modeling (e.g., Hugging Face ecosystem
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at the Institute and affiliated academic departments. What you’ll do: Designing, developing, and deploying modern AI/ML models—including deep learning, foundation models, multimodal architectures, and generative
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Informatics, Health Data Science, Biostatistics, or a closely related area. Strong ML/deep learning foundation plus expertise in at least one of: multimodal learning, time-series modeling, or NLP. Demonstrated
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genomic, epigenomic, and fragmentomic data, from patient liquid biopsy samples Design and evaluate deep learning models for MRD detection and characterization Collaborate with multidisciplinary teams across