22 biosignal-processing-machine-learning Postdoctoral positions at Harvard University
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data in near-term era quantum computers. Applicants with backgrounds in quantum information or particle physics are both encouraged to apply. Candidates with strong expertise in machine learning, quantum
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: Sitting using near vision use for reading and computer use for extended periods of time. Lifting (approximately 20 to 30 pounds), bending, and other physical exertion. As part of your application, we
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will lead and participate in observations and data analysis across the electromagnetic spectrum, or will lead work on machine learning classification of optical transients. Applicants with previous
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in Machine Learning, Computer Science, Electrical Engineering, Geophysics, Applied Mathematics, or a closely related field. Demonstrated strong research skills, evidenced by high-quality publications
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on type, size, location of data centers in the US, their electricity and water demand, carbon emissions; exposure to air pollution. ● Develop and/or apply methods for causal inference and machine learning
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and at national/international conferences. Collaborate with an interdisciplinary team of biostatisticians, computer scientists, and climate scientists. Contribute to open-source code, reproducible
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statistics, computing, machine learning (ML), and genetics and genomics, with a focus on large-scale genetic, genomic, and phenotype data. The work will involve both methodological research and collaboration
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Group invites applications for a Postdoctoral Fellow position. The research will develop and utilize emerging machine learning tools to remove bottlenecks in many-body simulations. Responsibilities: 1
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in internal meetings and at national/international conferences. ● Collaborate with an interdisciplinary team (bio)statisticians, data scientists, computer scientists, and climate scientists
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deep expertise in modern machine learning and a strong record of research accomplishment who are excited to advance foundation models, agentic systems, and new AI approaches for high-impact scientific