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- Delft University of Technology (TU Delft)
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Field
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Survey (ESWS) data from 9 European countries and a multilevel design with employees nested in teams nested in organisations. Advanced statistical techniques will be used to perform the analyses. You will
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experiments; collecting, processing and analysing eye-tracking and pupil data; contributing to the development of new experimental paradigms for studying attention; collaborating with PhD candidates and other
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PhD in Physics, Applied Science, or a related discipline Experience in statistical physics, stochastic processes and/or data analysis methods Strong interest in engaging and collaborating with
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Specific Requirements Requirements We are looking for a creative, rigorous, and independent scientist with a strong interest in the neural basis of social behaviour. The ideal candidate has: A PhD, or is
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an interdisciplinary and international team. You are organised, proactive, and motivated to contribute to research that has scientific, institutional, and societal impact. You have: A PhD in Health Sciences, Biomedical
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with academic partners. Job requirements We are looking for a highly motivated researcher with a strong background in atmospheric measurements and data analysis. You will have: A PhD in atmospheric
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, Mathematics, Bioengineering or a related discipline PhD in Physics, Applied Science, or a related discipline Experience in statistical physics, stochastic processes and/or data analysis methods Strong interest
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Postdoc in Application of eXtended Reality for Inclusive Automated Vehicle and Road User Interaction
. Conduct real-world testing to evaluate the effectiveness of inclusive eHMI design. Collaborate with fellow researchers, mentor PhD students, and contribute to ongoing projects and the development of the lab
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from clinical specimens; Develop, optimize, and apply DIA- and PRM-based proteomics methods; Perform quantitative data analysis, normalization, quality control, and statistical interpretation
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learning and expand its safe use in space. This includes, but is not limited to, approaches based on statistical mechanics and thermodynamics of learning, dynamical systems and continuous-time views