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Field
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digital identity system stack trustworthy; and design and execute qualitative and preferably quantitative empirical studies to measure the relationship between the objective trustworthiness safeguards and
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the advantages of in-depth qualitative and large-scale quantitative research with its sequential, mixed-methods toolkit. This includes focus groups (which have already been completed), Correlational Class Analyses
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combines this field experiment with qualitative, behavioral and economic research to understand not only whether these strategies work, but how, for whom, and at what cost. As a postdoctoral researcher, you
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and policies. Building on innovative research developed at the Erasmus Institute on Culture and Stratification, the project harnesses the advantages of in-depth qualitative and large-scale quantitative
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with user studies, preferably qualitative methods such as interviews, think-alouds or focus groups; significant coding experience in Python; experience with LLMs, in particular as part of agentic systems
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conditions, drawing on survey data, earth observation outputs, and qualitative inquiry from all consortium partners. Mentorship and capacity strengthening Contribute to the supervision and mentoring of PhD and
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interaction design, prototyping of interactive, mobile or wearable systems, and the design and evaluation of user studies (qualitative and/or quantitative) Experience translating empirical findings into design
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online and in-person/hybrid audiences; Excellent research skills, particularly in gathering and analysing qualitative data following ethical research practices; Experience publishing as lead author in
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/mobility studies, geography, environmental psychology, urban data science, or a related field. Experience with quantitative and qualitative monitoring, evaluation, and data collection and analysis methods in
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in urban design, urban planning, transportation/mobility studies, geography, environmental psychology, urban data science, or a related field. Experience with quantitative and qualitative monitoring