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system configurations, experimental setups and operational data. The scientific ambition is to develop methods that combine physical models and data-driven approaches for adaptive, real-time operation of
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, financial, legal and media conditions shape the cultural and creative industries across sectors. Applicants should demonstrate competence in qualitative research methods. Experience with quantitative and
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; survey design and online experimental methods; quantitative data analysis, preferably including choice modelling, willingness-to-pay analysis, segmentation, multivariate statistics, or related methods
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must include: a presentation of an original research question a description of the initial theoretical framework and method a presentation of the proposed material a work plan for the project Application
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duration of employment This is a 5-month position (30 hours/week) from 01 November 2026. Job description You will be contributing to the development of catalytic methods based on transition metal catalysis
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learning methods for medical image analysis, with a particular focus in anomaly detection and unsupervised learning. In this position, you will have the chance to explore basic machine learning research as
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validate digital-twin and optimization methods for electrolysis systems, working both independently and collaboratively with the group and with academic and industrial partners. In particular, you will
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interdisciplinary academic profile that combines competencies in energy systems analysis with qualitative social sciences methods, political sci-ences or similar. You possess strong analytical skills and experience
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the position. Your work tasks You will develop and validate digital-twin and optimization methods for electrolysis systems, working both independently and collaboratively with the group and with academic and
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be to develop wireless sensing and communication methods that are designed together with AI-based inference, rather than treating connectivity as a separate layer. Particular attention will be given