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-convex optimisation, performance analysis, machine learning, etc.) 1) Applicants should have, or expect to achieve, at least a master’s (or international equivalent) in a relevant science or engineering
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psychology; narratives and communication; Earth system risks and tipping points; or experimental and intervention-based social science. Experience with empirical data collection and analysis, participation in
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motivated to conduct laboratory work and have prior experience working in a chemical laboratory environment You are eager to use advanced data analysis tools (e.g. Python programming) Familiarity with
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candidate to carry out the following functions: Characterize by time resolved THz spectroscopy (TRTS) and THz time domain spectroscopy (THz-TDS) semiconductor samples. Lead the analysis of data and the
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health. Successful candidates may have experience in electron or X-ray microscopy, image analysis, AI and machine learning, quantitative data science or computational modelling. They will be able to work
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remain resilient in a world of increasing climate uncertainty? This PhD project offers the opportunity to work at the intersection of climate change, food systems, and advanced optimization modelling. You
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, reliability, and operational performance. As railway infrastructure ages and traffic demands increase, there is a growing need for innovative methods to continuously assess track condition and predict
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apomictic plants reproduce through clonal seeds, they allow us to disentangle genetic from epigenetic effects with unprecedented clarity. The project combines high-resolution analysis of genetic, epigenetic
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tasks, analyse behavioural data, apply computational modelling techniques, and write scientific articles that will form the basis of your dissertation. You will work closely with your supervisors, another
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Department BFI - Research About the Department The Becker Friedman Institute for Economics serves as a hub for cutting-edge analysis and research across the entire University of Chicago economics