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sampling of the parameter space of eclipsing binary observables, most notably photometric data from NASA’s Kepler and TESS missions. In parallel, the applicant will be given an opportunity to teach
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turbulence modelling, CFD mesh generation and use of parallel computing Have relevant experience in working with aerosols and droplets Proficient in handling large data sets and the ability to analysis and
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basic science with biotech applications and combines rich functional assays with cutting-edge computational biology. Our method relies on aptamer-based sensors to recognise each specific metabolite target
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Framework Programme? NextGenerationEU Reference Number 123/2025/BI/AgendasPRR Is the Job related to staff position within a Research Infrastructure? No Offer Description A call for application is now open for
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packages relevant to biostatistics and bioinformatics (e.g. R, Python, Fortran, Matlab, SAS, Stata or equivalent) and experience in the use of high-performance computer resources and parallel computation
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-Walls Centre – as well as Quantum Technologies Aotearoa (QTA), a national multi-institutional research programme. The Department also has particular strengths in affiliated areas through multiple active
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Postdoctoral Fellow - Materials Chemistry, Texas Materials Institute, Cockrell School of Engineering
or parallel reactors Collaborate with computational scientists to integrate machine-learning models for closed-loop materials discovery Collaborate with companion postdocs on functional materials
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will join Andrej Prsa’s research team and work on the PHOEBE code , advancing our understanding of the processes in contact binary stars. In parallel, the applicant will be given an opportunity to teach
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successful applicant will join Andrej Prsa’s research team and work on cutting-edge theoretical and observational aspects of binary and multiple stellar populations. In parallel, the applicant will teach
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model is employed to forecast renewable energy availability, providing crucial insights for the design optimization process. The ML-assisted operation tackles the dynamic optimization of parallel energy