27 image-processing-and-machine-learning "UCL" Postdoctoral positions at Harvard University
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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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. Specifically, our core technique is scanning electrochemical cell microscopy (SECCM) , a powerful electrochemical imaging method for probing electrochemical processes at nanoscale sites on complex electrodes. We
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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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voltage imaging and targeted optogenetic stimulation in larval zebrafish. The system will combine a high-speed light-sheet microscope, a two-photon holographic stimulation system, and software
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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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Models Basic Qualifications: A Ph.D. or equivalent degree in Machine Learning, Computer Science, Electrical Engineering, Geophysics, Applied Mathematics, or a closely related field. Demonstrated strong
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experience with bacterial husbandry, cloning, genetics, microscopy/biological imaging and/or protein biochemistry. A strong publication record in microbiology/molecular biology demonstrating these skills is
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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