Aufgaben Examine through qualitative methods how workers build personalised routines around agentic and generative AI and how these routines affect collaboration, oversight and resilience. Compare bottom-up experimentation and shadow AI with formal top-down rollout. Combine longitudinal cases, observation, digital diaries, interviews and collaboration-network analysis. Develop a framework and practical guidance on disclosure, versioning, audit trails, handovers and workflow portability. Refine the research questions and design with the supervision team and prepare ethics, data-management and open-science materials. Complete a doctoral thesis and related research outputs under the host doctoral programme and EMANAIRE plan. Take part in local and network-wide training, cohort activities, reviews, dissemination and practitioner engagement. Undertake the six-month intersectoral secondment and the separate academic mobility visit. Contribute to research integrity, equality, responsible AI, open science and FAIR data practices. Complete at least 20 ECTS credits of structured doctoral training, including at least 10 ECTS credits through network-wide provision. EMANAIRE combines local doctoral courses with three residential schools, monthly paper-development seminars, work-package workshops and network reviews. A Personal Career Development Plan will be agreed within the first three months and reviewed every six months.
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