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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 1 month ago
; understanding of the potential career trajectories of postdocs in science and engineering; familiarity with career services’ online technology/management systems; knowledge of career development theory and the
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Experience1 - 4 Additional Information Eligibility criteria - PhD on steganalysis and/or watermarking - knowledge in image coding and on generative methods - knowledge about python Additional comments NA
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/Province São Paulo City São Paulo Website http://www.fapesp.br/oportunidades/ Street R. Pio XI, 1500 - Alto da Lapa Postal Code 05468-901 STATUS: EXPIRED X (formerly Twitter) Facebook LinkedIn Whatsapp More
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Paulo City São Paulo Website http://www.fapesp.br/oportunidades/ Street R. Pio XI, 1500 - Alto da Lapa Postal Code 05468-901 STATUS: EXPIRED X (formerly Twitter) Facebook LinkedIn Whatsapp More share
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large language models Statistical learning theory and complexity analysis Automated theorem proving and formal methods
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Science Foundation (NSF) grant entitled “RTG: Combinatorics, Geometry, Representation Theory, and Topology” at the University of Oregon. Applicants should be in research areas connected to one of these four
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Job Description Apply now Req Id: 39190 Job Title: Post Doc Research Associate City: West Lafayette Job Description: Job Summary The High Energy Theory group at Purdue University invites
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State University (NCSU), starting in the Fall of 2026. The position is in the field of non-perturbative quantum field theory, with a focus on the physics of confinement, phases of matter, and particle and
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for multimodal sense-making (how different modalities combine to create meaning). Tasks will include contributing to project theory and methods development for the analysis of multimodal communication in song
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for better training and novel network designs. Low Effective-dimensional Learning Models. We will extend foundational theory of how large ML systems can be regularised to have dramatically fewer trainable