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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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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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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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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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" (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
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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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crucial insights. In this project, you will contribute to the development of AI-driven methodologies for experimental fluid mechanics , focusing on: Designing multi-fidelity neural networks for adaptive
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development of electrochemical sensors detecting environmental pollutants, providing real-time information for effective management. Past and current work includes electrochemical sensors for airborne virus
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, biocompatibility or the ability to reach deep brain areas. To solve this problem, we developed Ultra-Flexible Tentacle Electrodes (UFTEs), consisting of fibers one order of magnitude smaller than hair (2.4 um x 7 um