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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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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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curate data from live organisms sampled with RODI. Actively contributing to iterative improvements of RODI, including both hardware and software developments, based on field and analytical feedback
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You will have access to teaching assistants, research and travel funding, an international network of collaborators, and the wide variety of ETH career development resources The working language is
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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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research network spanning Prof. McDonald's career accross four continents (USA, Switzerland, Australia, UK) An inclusive and supportive team that values diversity Flexible working hours and partial home
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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 stakeholders
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conferences. The postdoc will benefit from ETH Zurich’s strong interdisciplinary and global network, including: The Albert Einstein School of Public Policy, The ETH’s Energy Science Center and ETH's World Food
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electrophysiological recordings to track how tumor invasion alters neuronal network activity at single-cell and population levels Apply spatial transcriptomic techniques to map gene expression changes in tumor-connected
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conclude on December 31st 2029. The goal of this research effort is to apply machine learning (ML) techniques, in particular (equivariant) graph neural networks to accelerate the creation of all physical