297 web-development-"https:"-"https:"-"https:"-"Institut-Agro-Rennes-Angers" positions in Switzerland
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travel behaviour data or urban mobility patterns Good German language skills (at least B2) Experience with collecting own data (e.g. questionnaires; measurements) Experience with developing networks with
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to be a place that promotes independent thinking and an environment that inspires excellence. Located in the heart of Europe, yet forging connections all over the world, we work together to develop
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Preprocess, annotate, and manage a rich multimodal dataset, applying both qualitative and quantitative analytical methods to model clinician attention, verbal reasoning, and documentation behaviour Develop and
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and flow field interactions Tuning of the CFD models with experimental results Artificial Neural Network training and development Scientific publications in journals and at conferences Supervision
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Your position A fully funded PhD position is available in the Computational Pharmacy group at the University of Basel. The successful candidate will contribute to ongoing research on the development
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environment with advanced laboratory infrastructure, the candidate will have a unique opportunity to develop their research abilities The position is available from May 2026 onwards Workplace Workplace We offer
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80%, Zurich, fixed-term The Laboratory of Molecular Medicine, led by Prof. Yohei Yamauchi, is developing the ViViD-AFM V2.0—a next-generation nano-imaging system. This project focuses
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industrial partners to tackle advanced modelling, simulation, sensing, and data analysis challenges in engineering systems across sectors. Project background The COMBINE Doctoral Network aims to train a cohort
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polymer additive chemistry. A more recent focus of the group is the development of sustainable polymer and additives. To strengthen activities in this area, we investigate development of functional covalent
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" (D2M). This innovative project is a collaboration between the University of Basel, the Bern University of the Arts, and the FHNW. The goal is to develop a highly automated, reproducible pipeline