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multidisciplinary teams (bioprocess engineering, omics analysis, modelling). Supervise and support PhD students and laboratory technicians. Where to apply Website https://eurecat.talentclue.com/en/node/123629429
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(at least 3 months). Experience in development and optimization of lipid nanoparticles. Experience in cell cultures and nanoparticle assays. Experience in in vitro models of ocular tolerance. Experience in in
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for the long-term health of our code repositories and the versioning of models and data (Git, DVC, MLflow). Requirements: Education: Ph.D Degree in Computer Science, Engineering, Mathematics, Physics
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at CRAG (from basic science to applied research using plant experimental model systems, crops and farm animals) make extensive use of genomic technologies and large sets of genetic and genomic data (https
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phenomenology in the Standard Model and beyond (including collider, flavour, Higgs and neutrino physics), QCD and strong interactions, lattice QCD, effective field theories in hadron and nuclear physics
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of the proposal. This includes the complete modeling and simulation of the response of the detectors to gravitational waves of different sources, generating realistic sensitivity calculations and exploring
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at CRAG (from basic science to applied research using plant experimental model systems, crops and farm animals) make extensive use of genomic technologies and large sets of genetic and genomic data (https
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coalescence events and determining their mass/spin spectrum, and for primordial black holes and axion-like particles as candidates for dark matter. We perform tests of exotic models for Gravity beyond General
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or Python (data management, statistical modelling, visualisation, and reproducible workflows). Excellent English (written and spoken); Spanish and/or Catalan are an asset, and additional European languages
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on reinforcement learning to optimize cooling and trapping parameters using real-time feedback for the Rydberg atom experimental platform. Enhance holographic methods for optical tweezers with generative models and