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Details Title Postdoctoral Fellowships in Networking Support for Machine Learning School Harvard John A. Paulson School of Engineering and Applied Sciences Department/Area Computer Science Position
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may bring a new technology or perspective to bear on the work in the lab. The successful candidate will work with the Principal Investigator Dr. Peter Sorger and Jeremy Muhlich, Director of Software
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cutting-edge theories, methods, and computational tools for integrating large-scale, heterogeneous biomedical data across multi-institutional research networks, with a focus on the analytical and
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, working alongside postdocs, research staff, and students, as well as with collaborators and established software development teams. Our interdisciplinary lab represents a convergence of microbial ecology
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data across multi-institutional research networks, with a focus on the analytical and computational challenges arising in precision medicine, mental health, and biomedical informatics. The postdoctoral
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in scientific research, which can be evidenced through publications, technical reports, or impactful software projects. Additional Qualifications Interest in applying programming language theory
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of complex systems, networks, and large-scale data Machine learning, generative AI, NLP, or algorithmic decision systems Ideal applicants will have a strong background in operations research, statistics
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such as: Causal inference and the design and analysis of experiments Reinforcement learning and sequential decision-making Analysis of complex systems, networks, and large-scale data Machine learning
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have expertise in one or more of the following areas: Sustainable building systems or materials science Power system modeling, control systems, or HVAC alternatives IoT and sensor networks in the built
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-photonic computing architectures; Silicon-photonic network architectures Machine Learning Algorithms/Systems: Experience in design and use of ML algorithms; Experience in using ML for designing computing