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• PhD in Mathematics/Statistics/CS. Skills: • Proficiency in reinforcement learning, quantitative finance, risk management, and machine learning. • Strong programming skill in Python
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. Strong experience integrating EEG with non-invasive electrical brain stimulation, particularly tRNS. Strong experience investigating individual differences in learning and cognition Experience applying
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Center for Drug Evaluation and Research (CDER) | Silver Spring, Maryland | United States | 1 day ago
of verbatim data. As an Oak Ridge Institute for Science and Education (ORISE) participant, you will join and learn from a community of scientists and researchers engaged in an effort to use natural
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learning, and computational biology to adapt and augment existing open models for predictive biology. Approaches may include fine-tuning, parameter-efficient tuning, probing, and retrieval- or knowledge
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texture measurements on vegetable tissue. You will participate in data collection and analysis and collaboratively advance the development of standard operating procedures for advanced microscopy. Learning
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representation through machine learning. The position is for a fixed term of 3 years and is part of the project “Reaching AI Projections Trustworthy for Unseen Rainfall Extremes (RAPTURE)”, funded by a European
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Assurance and Insurance, Quantum Trust, Privacy-preserving Machine Learning, Privacy-preserving Multi-party Computation, Verifiable Computation, Trustworthy Systems, Adversarial Machine Learning, Assessing
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, and sub-daily to evolutionary time scales. One of the goals of the SCINet Initiative is to develop and apply new technologies, including AI and machine learning (ML), to help solve complex agricultural
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Center for Drug Evaluation and Research (CDER) | Silver Spring, Maryland | United States | 1 day ago
therapeutics and generic drugs. This effort covers more than just medicines. Research Project: As an Oak Ridge Institute for Science and Education (ORISE) participant, you will join and learn from a community of
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are particularly interested in: machine learning for molecular and omics data, including representation learning for biological sequences and structures, and the integration of multiple omics layers machine learning