15 postdoc-in-thermal-network-of-the-physical-building PhD positions at University of Cambridge
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Our scientists, from professors to postdocs to students, hail from all corners of the world, representing over 80 different nationalities, and building on research and degrees earned from the finest
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. The research will probe beneath surfaces to detect impurities and internal features, while also exploring new methods for embedding invisible authenticity markers. Finally, advanced data tools and neural network
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engineering, computational neuroscience, artificial neural networks and bio-inspired robotics: "Rhythmic-reactive regulation for robotic locomotion" (Supervisor: Prof Fulvio Forni) will apply techniques from
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This is a four-year (1+3 MRes/PhD) studentship funded through the Cambridge EPSRC Centre for Doctoral Training in Future Infrastructure and Built Environment: Unlocking Net Zero (FIBE3 CDT). Further
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building blocks to elicit properties far beyond simple averaging over the component materials involved, instead giving exciting opportunities for new functionalities that are not found in natural materials
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combines hands-on training, cohort-based learning, and cutting-edge research, preparing graduates for careers in academia, industry, startups, and beyond. We welcome applicants from the Physical Sciences
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proteins. Moreover, you will determine whether the success of such alternations depends on protein family and on mRNA characteristics such as codon optimality. You will construct a panel of engineered cell
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themes that span the scientific research spectrum from basic science to population health and respond to current scientific needs in biomedicine. BSU researchers have worked extensively to make inference
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also throughout the development phase, which involves transforming a molecule into a medicine and addressing various chemistry, manufacturing, and control (CMC) challenges. A key aspect of this process
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earlier as well as understand the mechanisms driving CIN. This project will aim to build upon previous work within the group developing computational methodologies to detect and deconvolute the mutational