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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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Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3S) | Portugal | 3 months ago
fellowship Job Status Full-time Hours Per Week 35 Offer Starting Date 27 Nov 2025 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Reference Number Fellow_BI
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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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for large samples at ESRF ID16A using multislice tomography approaches. You will lead the development of and work with parallelized computer models to simulate how coherent waves travel through materials with
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) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research
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Heikkilä at Jyväskylä University. In parallel, the NANO group is pursuing work on fractional Chern insulator in twisted TMD materials. Since there is clear synergy between twisted graphene and twisted TMD
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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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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