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will design and implement differential privacy solutions for large-scale scientific data models in federated learning environments. You will advance privacy-preserving machine learning by developing
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Science Statistics / Biostatistics Applied Mathematics Data Science Demonstrated expertise in modern machine learning, including at least one of the following: Deep learning (e.g., transformers, sequence models
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of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI implementation. Ability to apply AI to interdisciplinary research or developing AI models
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the
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Engineering Department at The Pennsylvania State University. This position involves the development and application of numerical analysis approaches using Machine Learning based multi-physics tools
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for implementation, data collection, and teacher co-design activities. Please Note: This position is grant funded; future employment may be contingent upon future funding. Qualifications Required Education PhD in
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and machine-learning potentials for planetary materials. Curating and generating large-scale ab initio datasets across wide pressureâ“temperature regimes. Designing and training advanced machine
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and algorithmic perspectives on large language models Statistical learning theory and complexity analysis Automated theorem proving and formal methods Random matrix theory and its applications in modern
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development and evaluation. This opportunity will prepare candidates for a range of competitive positions in academia or industry that involve machine-learning for biological or chemical data, computational
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research Managing and analyzing data, implementing machine learning algorithms on data Conducting literature reviews Preparing presentations, manuscripts, and grant submissions Assisting with research