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approaches to microbiome data Harmonization and management of clinical metadata across cohorts Integration of clinical and multi-omic datasets to uncover microbiome–host interactions Strong statistical skills
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within a Royal Society-funded project to investigate symplectic topology and its interaction with low dimensional topology, mirror symmetry, and geometric representation theory. The University of Sheffield
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of Master and supervising PhD students. Fluency in English as well as a proven ability to write scientific articles are also mandatory. The post-doctoral fellow should be an effective team player to interact
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strong scientific interaction with CRG laboratories and infrastructures in Barcelona. This first call aims to recruit two outstanding researchers who will lead the initial phase of this initiative. Beyond
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researcher to independently drive research projects, collaborate across disciplines, and help foster an interactive and dynamic research community. Research in the Alexander Lab spans scales of biology as
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detailed research records and files. Testing software prototypes and interacting with fellow researchers in order to perform job functions. Performing basic data analysis to produce reports and contribute
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population of high achievers, and interaction with world-leading faculty across a range of disciplines. Postdoc applicants should get in touch at suhaib.fahmy@kaust.edu.sa, attaching a CV and discussing their
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the interactions between ink and fiber, optimize track architecture, and ensure long term functional stability. This research direction is highly promising due to its broad applicability in health monitoring, sports
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sociology. Strong quantitative skills and experience with large-scale data analysis required. Computer Science/HCI: PhD in Computer Science, Human-Computer Interaction, Information Science, or related
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for light–matter interaction in hyperuniform disordered plasmonic structures, including electromagnetic modelling, optimisation of metal–dielectric–metal resonators, and physics-informed machine-learning