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, transfer learning, federated learning, data integration, algorithmic fairness, survival analysis, and methods for heterogeneous and multi-source data. Training Environment and Career Development
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analytical many-body calculations Expertise in one or more of the following: computational methods, software and algorithm development, high-performance computing, and data analysis Proficiency in relevant
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biomedical data science, developing new methodology and scalable algorithms, and collaborating with interdisciplinary teams at Duke. Duke is an Equal Opportunity Employer committed to providing employment
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We are seeking a postdoc to co-design efficient and realistic simulation algorithms for noisy quantum circuits in superconducting hardware, combining quantum modeling with hardware-aware performance
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.) ● Demonstrated experience in method and tool development (e.g., new algorithms, tools, or computational frameworks) ● Evidence of interdisciplinary research, bridging computational and biological domains
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or related areas. No prior knowledge of cryptography is required. Expertise in optimization or efficient algorithm design will be considered an asset. Applications should include a CV, a list of publications
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--computational experience in simulating/implementing quantum algorithms for field theories on quantum hardware. Duke is an Equal Opportunity Employer committed to providing employment opportunity without regard
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and data processing skills: experience of programming in one or more languages (e.g. R, C/C++, Python, Matlab). Practical experience of algorithm development and implementation of machine learning
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Computer Science, Chemistry, Chemical Engineering, Physics, or Materials Science. You will develop optimisation and machine-learning algorithms for human- and literature-informed discovery of new materials
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“algorithmic bias” in AI systems; understanding what it would be to “align” AI systems with ethical norms; developing and evaluating proposals for the governance of powerful AI systems; the ethical issues raised