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methods, which could include but are not limited to: Kriging surrogate, Polynomial Chaos Expansion (PCE), and Physics-Informed Neural Networks (PINNs) Contribute to the strategic direction of research
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methods and systems to overcome the limitations of today’s materials processing technologies. One particular area of interest is the design and fabrication of active structures through hybrid production
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to evaluate emerging instrumentation and software and translate them into robust, scalable workflows. Alongside method development, the Zamboni Lab is tightly embedded in numerous collaborations with academic
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Design and microfabrication of soft optical fibers densely distributed with actuators Develop actuator multiplexing methods for individual addressability Demonstration for advanced robotic functions as
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it independently You must be willing to exchange with your fellow team members, support them, and learn from them Non-traditional research topics, questions and methods are encouraged We also encourage
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cutting-edge technologies to dissect these interactions: high-density microelectrode arrays (HD-MEAs) for large-scale electrophysiology, spatial transcriptomic methods, and human iPSC-derived neuronal
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also be opportunities to design novel bioelectronic implants and develop specialized control software Profile We are seeking highly motivated applicants with a background in physics, material science, or
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pipelines to support the labs long-term research interests Proactively manage the labs genomic data resources Supervise and mentor PhD and Masters students in comparative fungal genomics Assist in
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: Experience with analyzing GPS tracks Good data-handling skills and ability to use R (compulsary) and preferably also Python and/or GIS competently Statistical/causal inference knowledge PhD degree in a related
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Zurich, PSI, and international partners More information about the project and the group can be found on our website . Starting date: flexible in 2026 Employment: full-time Profile PhD in physics or a