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well as their targeted development into cells with gas diffusion electrodes for the reversible adsorption of carbon dioxide. You will characterize functional porous carbons using techniques such as XRD, SEM, Raman
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engineering, synthetic biology, and the development of improved microbial strains for applications in industrial bioprocesses, bioeconomy, and sustainability. Key Responsibilities Design and implement genetic
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position is associated with the Robot Navigation and Perception Lab (https://rnp.aass.oru.se ) which belongs to the Centre for Applied Autonomous Sensor Systems (AASS) research environment (https
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
/Description: The Miao Lab in the Computational Medicine Program and Department of Pharmacology at the University of North Carolina – Chapel Hill (https://www.med.unc.edu/pharm/directory/yinglong-miao/) is
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in computer vision, machine learning, and related fields, to further visualization and interpretation of molecular images. Our research environment focuses on methodological development in cryo
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strongly encourage applications from candidates with diverse backgrounds, including those from groups that have been underrepresented in academia. To apply, visit https://wesleyan.wd5.myworkdayjobs.com
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close synergy with partners. Activities: Design and synthesis of photocatalytic materials Development of semiconductor nanoparticles (sensitized or not with grafted dyes) and molecular systems
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opportunity to be an active part in the establishing of our department Comprehensive training programs and individual opportunities for personal and professional development Access to a strong research network
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minimum of 12 months Appointment Start Date: Early/mid 2026 Group or Departmental Website: https://evodesign.org/ (link is external) How to Submit Application Materials: Please directly contact us at
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for this position will work as a member of an interdisciplinary team on research involving the use of statistical and machine learning methods for the development of Immune Digital Twins. There will be potential