220 computational-physics "https:" "https:" "https:" "https:" "IFM" uni jobs in Switzerland
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focusing on the physical realization of a next-generation high-speed AFM system tailored for biological research. Job description The Lead Mechatronics Engineer is responsible for the physical realization
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Architecture & Roadmap: Define the technical vision and high-level design for ViViD-AFM V2.0, ensuring a scalable and robust system architecture. IP Strategy & Novelty Design: Drive the design process to ensure
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agreement. We develop computational methods to accelerate materials discovery through defect engineering, with a focus on extreme environments. Application areas include fusion reactors, hydrogen systems, and
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, engineering, physics, or a related field, and with strong interest in the cryosphere. The successful candidate has experience in computational data analysis or numerical modelling. You are eager to work
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the approval process in AVA, the UZH third-party funding platform . To receive the confirmation letters in time, please start the approval process no later than 22 October 2025. Here you can find instructions
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of astrophysical and/or space-science data processing. The successful candidate will hold an MSc or greater degree in computer science or engineering, or a PhD in applied mathematics, physics, or related fields
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, mechanical or electrical engineering, geosciences, physics, applied mathematics, computer sciences or related fields, and be at the beginning of their research career. Principal qualifications include strong
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organised individual to support multiple experimental and computational research groups. The successful candidate will contribute to both a collaborative research network and institute-wide activities
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100%, Basel, fixed-term The Computational Biology (CoBi) group, led by Prof. Dagmar Iber, develops data-driven, mechanistic models of biological systems using advanced imaging and computational
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-phonon coupling elements. With these, dedicated scattering rates can be computed and then used in quantum transport simulations. Down the line, we aim to pre-train a common GNN backbone model capable