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Equation, Stochastic simulation algorithms, and approximation methods. ● Experience with single-cell or spatial transcriptomic data analysis. ● Familiarity with machine learning and deep learning
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analysis. ● Prior work on kinase or other signaling-protein conformational dynamics, phosphorylation-driven activation, or allosteric regulation. ● Familiarity with machine learning and deep learning
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
following areas: High-dimensional probability and concentration/functional inequalities Markov processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations
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in our collections or on view in our galleries. We seek an individual with a passion for teaching and interdisciplinary thinking; a deep commitment to object-based learning and inquiry; and a
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network inference and modeling, machine learning and deep learning. Experience in working with Arabidopsis and plant genome data is a strong plus. The position is expected to continue for multiple years
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concentration/functional inequalities Markov processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large language models
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of machine learning for healthcare and related topics Deep knowledge of multi-modal learning, transfer learning, foundation models, and self-supervised learning. Experience in dealing with large
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: ● Applicants must have a PhD in Computer Science or related field, with no more than five years post receipt of the PhD. ● Experience in one or more ML domains, such as deep learning, reinforcement learning
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ML domains, such as deep learning, reinforcement learning, or human-centered ML. Proficiency in programming languages (e.g., Python) and ML frameworks (e.g., TensorFlow, PyTorch), with evidence in