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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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the eBird project, one of the largest sources of avian biodiversity information in the world. The eBird team is a collaborative and innovative group that includes staff with deep expertise in bioinformatics
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team is a collaborative and innovative group that includes staff with deep expertise in bioinformatics, statistics, and ornithology. The candidate will also have the opportunity to work closely with
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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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and deep learning-based digital twins. Stipend: The selected faculty participant will receive a monthly stipend commensurate with their institutional salary. Program Requirements: To document
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Researcher will work in Professor Benjamin Peherstorfer’s group (https://cims.nyu.edu/~pehersto/ ) on scientific machine learning at the Courant Institute of Mathematical Sciences where they will help
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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