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Field
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to diverse academic and industrial audiences. Proficiency in Python and deep learning frameworks such as PyTorch. Experience with Linux environments and GPU cluster management is essential. Competent in
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and the ability to publish high-quality academic outputs. You are expected to have proven experience in developing and applying predictive deep-learning models for data-driven analysis and forecasting
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, and the ability to read related scientific papers on cancer combination therapy. It would also require expertise in relevant AI methodology, such as deep learning architectures for property prediction
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command of data wrangling, cleaning, and large-scale dataset management. Machine Learning/Deep Learning: Experience with PyTorch, TensorFlow, or Hugging Face; embedding models; and model validation
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The postdoctoral fellow will lead and co-lead projects that combine computational modeling, machine learning, and EEG to answer questions about scene understanding and neural representation. The fellow will work
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to equity, excellence and impact. VU’s vision, outlined in our strategic plan Start Well Finish Brilliantly (2022–2028) , is to be a global leader in dual-sector research and learning by 2028. Our strategic
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Details Title Postdoctoral Fellow in On-Premise Computing for Autonomous Vehicles (Computer Architecture, Machine Learning and Runtime Systems) School Harvard John A. Paulson School of Engineering
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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
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of excellence and a culture marked by ambition and a deep, practical engagement with challenges facing society. We continue to produce versatile alumni and draw faculty and staff eager to be a part of the
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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