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to work in a large international collaboration while advancing your own research; and a good command of spoken and written English. Experience with data analysis in astroparticle, particle or cosmic-ray
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system analysis . Rigorous formal methods will ensure that learnt machine learning methods and the resulting systems are indeed resilient, aiming at improved dependability and trustworthiness. Formal
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physics, or a related discipline. • A genuine interest in combining laboratory experiments, fieldwork, and quantitative data analysis. • Enthusiasm for interdisciplinary research and collaboration within
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intelligent built environments, and analysis of behavioral and physiological data. The work also includes the development of personas, the use of LLM‑enhanced decision narratives, and agent-based social
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competence in quantitative and statistical methods, including applied econometrics and panel data analysis, proficiency in a statistical programming language such as R (preferably), Python, or Stata. Prior
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PhD Scholarship in University Students’ Learning Via Digital Twins of Neutron Scattering Instruments
studies and data analysis with one of the leading groups in the field. Qualified applicants must have: An MSc degree in Physics, Nanoscience, Chemistry, Engineering, or Material Science, or Educational
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data from textual sources Conduct data analysis using econometric and statistical tools (STATA, R, or Python). Assist in literature reviews and summarising academic research. Contribute to writing
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and statistical methods, including applied econometrics and panel data analysis, proficiency in a statistical programming language such as R (preferably), Python, or Stata. Prior exposure to firm-level
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grounded insights into the aesthetic, lived, and institutional realities of contemporary world literature and its agents. Combining textual analysis with sociological and ethnographic methods, EMLIT aims
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statistical model calibration, machine learning and data analysis. The research environment is international and interdisciplinary, with close links between fundamental method development and technically