4,031 systems-engineer-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" Postdoctoral positions
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, fungi, and other microorganisms in forest and agricultural systems. Our key research areas include forest and agricultural plant pathology, plant-soil-microorganism interactions, microbial and
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-edge methodologies to evolve, engineer and study microorganisms capable of growth on CO 2 and derived feedstocks. Your goal will be to develop robust microbial strains for C1-based biomanufacturing
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Energy-Water Systems (I- CREWS ), which seeks to increase understanding of how physical infrastructure, data, governance, local knowledge, and community context shape resilience under meteorological
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Associate in Weed Science and Forage Systems. The successful candidate will contribute to an interdisciplinary research and extension project evaluating the impact of weed composition and biomass on alfalfa
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development ( https://rupress.org/jcb/article/220/11/e202103080/212704/ ) or regeneration ( https://www.science.org/doi/10.1126/sciadv.adi7840 ). To address these questions, we use mouse and cell
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-Doctoral Associate in Internet Measurement and Equitable Web Systems. Our lab develops measurement-driven approaches to understanding and improving the Internet and the Web. Our research spans Internet
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facilitate the above research of gene regulation. The homepage of Li Lab can be found at https://www.stjude.org/people/l/chunliang-li.html . Recent selected publications from Li Lab can be found below (2023
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King Abdullah University of Science and Technology | Saudi Arabia, | Saudi Arabia | about 4 hours ago
developing resilient, nutrition-sensitive food systems that can support global health in a rapidly changing world. We invite applications for a Postdoctoral Researcher experienced in Marine Biogeochemistry
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King Abdullah University of Science and Technology | Saudi Arabia, | Saudi Arabia | about 4 hours ago
Continual Language Learning , ( ICLR ’20), Yuanpeng Li, Liang Zhao, Ken Church, Mohamed Elhoseiny, Code: https://github.com/yli1/CLCL Uncertainty-guided Continual Learning with Bayesian Neural Networks
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Position Summary The TARDIS collaboration is actively applying and extending the Monte Carlo radiative-transfer open-science framework TARDIS (https://tardis-sn.github.io/) to model the physics of