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Field
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adaptive systems that jointly optimize morphology and control for real-world physical interaction. Requirements Ph.D. in Computer Science or a related discipline. Strong background in Machine Learning
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perturbation models across diverse single-cell datasets Collaborate closely with Helical-AI on scaling, optimization, and integration of the developed models within an HPC-enabled industrial pipeline Publish
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experimental animals Experience implementing and optimizing eye tracking, body tracking, and/or visual reality environments Proficiency in Python and scientific computing libraries Familiarity with spike sorting
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exciting opportunity for someone who thrives in an interesting and challenging work environment. Core responsibilities include: Research in the intersection of network optimization and approximation
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in computer vision, machine learning, and related fields, to further visualization and interpretation of molecular images. Our research environment focuses on methodological development in cryo
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Swarm Intelligence, Reinforcement Learning and Optimization Techniques. As a Postdoctoral researcher, you will: Lead cutting edge research in Swarm Intelligence and Machine Learning, addressing challenges
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in materials for electrochemistry. While the focus in on computational expertise, this position will involve some experimental work in adapting workflows for automation and artificial intelligence
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productivity (publications, presentations, software, system prototypes) Strong analytical and computational skills (e.g., Python, R; simulation or optimization tools) Experience working with real-world datasets
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exciting opportunity for someone who thrives in an interesting and challenging work environment. Core responsibilities include: Research in the intersection of network optimization and approximation
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modeling, control, and optimization of power and energy systems with applications to maritime and coastal infrastructures (e.g., shipboard microgrids, port facilities, islanded communities, desalination