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CFD workflows and Lagrangian particle/cell tracking to extracting actionable insights with statistical learning and AI/ML—ultimately enabling more robust scale‑up, smarter process control, and faster
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and statistical tools to identify patterns in large datasets The candidates should demonstrate evidence of self-driven and independent research capability, excellent collaboration and communication
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members to analyse data, present project findings at international conferences, and produce scientific papers. Some research-centered teaching at BSc and MSc levels as well as science outreach for public
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the industry partner involved in the project Contributing to grant deliverables, presentations, and reporting Write high quality scientific manuscripts Contribute to our education program Train BSc, MSc and PhD
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bioinformatic and ecological statistical tools To be able to collaborate with other researchers, understand biological scientific questions and provide analytical solutions to complex datasets To work closely
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, supported by a good understanding of urban water systems, water quality, environmental chemistry and statistics. You should have experience in the development and application of water quality models, with
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conducting work overseas (Africa or Latinamerca) is desirable. • Experience conducting statistical analyses of experimental datasets, and strong analylical skills (proficiency in R, Python, or Matlab
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or Latinamerca) is desirable. • Experience conducting statistical analyses of experimental datasets, and strong analylical skills (proficiency in R, Python, or Matlab). • Publication record appropriate to stage