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Field
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intelligence. This part focuses on robotic agents operating in unstructured 3D environments (homes, offices, industrial sites, shopping malls). The work centres on (a) model-based and hybrid RL algorithms built
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for the quantum era. The successful candidate will conduct interdisciplinary research on topics including: Security of quantum algorithms, quantum software, and quantum networks Quantum-safe cybersecurity
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the supervision of Dr. Jun Xu Develop high-quality models, algorithms, experiments, or integrated modeling-characterization frameworks Publish first-author papers in leading peer-reviewed journals Contribute
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algorithm design skills Expertise on ML tools for chemistry, in particular, generative AI Experience with Python, ML, and AI for chemical applications Job Description: A Post-doctoral Associate in Theoretical
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nanostructures and device optimization using simulations. The goal is to develop precise control of specific emission properties such as polarization, color, and angular distribution while simultaneously
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research and implementation. You will also develop novel algorithms using state-of-the-art computer vision and machine learning techniques (segmentation, multimodal AI, foundation models, agentic frameworks
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). - Quantum algorithm design. - Error correction or error mitigation. Certifications/Licenses Required Knowledge, Skills, and Abilities Strong background in quantum chemistry, computational physics
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the molecular biology of aging and neurodegenerative diseases. Engage in the development and testing/validation of new algorithms and their applications to transcriptomic, epigenomic, genetic, and proteomic data
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experimental cycle from real-time X-ray measurements to post-experiment reconstruction: Develop learning-enabled algorithms for 3D reconstruction of noisy and heterogeneous synchrotron datasets. Implement
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quantum chemical methods and machine learning; developing quantum algorithms for computational chemistry on quantum computers; and applying existing and new computational methods to study multiscale