90 computational-physics-"https:"-"https:"-"https:"-"https:"-"Dr" positions at ETH Zurich
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environment. Job description Develop and implement ecological process formulations within the Forest Studio modelling framework Design modelling strategies, conduct testing and validation, and ensure
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In the Optical Nanomaterial Group, we specialize in the nanofabrication and optical characterization of TFLN photonic circuits. While the fabrication process is at an advanced stage, further
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for key program deliverables. Drive the development of high-impact deliverables, such as the annual report and the Phase III Outline Proposal. Coordinate input across projects, synthesize insights, and
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Switzerland for more than 12 months in the 36 months immediately before their recruitment date. The student will be enrolled in the structured PhD programme of the Department of Mechanical and Process
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their recruitment date. The student will be enrolled in the structured PhD programme of the Department of Mechanical and Process Engineering or the Department of Health Sciences and Technology
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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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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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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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, 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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-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