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the aim of developing models, tools and competitive technological solutions to support the transition in sectors such as energy, transport and heavy industry. Within a scientifically excellent environment
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languages (for example, for hardware design or parallel programming) Array or tensor programming Applications to physical sciences, dimensional analysis, or climate impact modeling It is highly meritorious
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with fungal community, genomic or transcriptomic analyses Knowledge of multivariate analyses and statistical modelling Excellent communication skills in written and spoken English We consider it a merit
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learning research. What you will do Propose, develop, and evaluate advanced machine learning models, including reinforcement learning methods, for the energy-aware coordination of EV fleets. Publish high
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for science and discovery. The ideal candidate should also have at least 1 first-authored publication in press, and have experience working with murine models in a laboratory setting. Minimum education
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-validation by droplet digital and real-time PCR. Contribute to the host–microbiome analyses (GWAS/mGWAS, metagenomics) and the integrative modeling led by the graduate student. Apply machine-learning and AI
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Modeling, Musculoskeletal Modeling, Wearable Sensors All offers of employment are contingent upon successful completion of a background check. The University of Oregon is proud to offer a robust benefits
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other computer related tasks, such as Microsoft Office. Preferred Special Knowledge, Skills and Abilities: -ImageJ and Matlab. -Expertise in biosensors or protein design. -Familiarity with genetic model
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research for an NIH-NSF funded project on human health, movement, and infectious diseases. Successful applicants will have experience in: Either spatial analysis using GIS or disease modeling in R and/or
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for device characterization Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork Preferred Qualifications Ideal candidates will have experience in one or more of the