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- Prof. Dr. Ricarda Toerner
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Field
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, systems, and hardware design. Experience in one or more of: LLMs, AI agents, embedded ML, physical modelling and simulation Strong programming skills in Python and C/C++, familiarity with ML deployment
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near-instantaneous proliferation of comb lines and new regimes of spectral control. Project background This project will combine advanced numerical modeling with laboratory demonstrations to explore
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in physics-based modeling at multiple scales. We bridge the virtual to the real world by multi-parameter sensing and creating digital twins of heat-sensitive biological systems (food, humans) that can
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lack of rapid tools to understand and monitor the spread of pathogens. Building on our previous work on DNA tracing technologies, we aim to develop tools and procedures to model and monitor the spread
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with ex situ experiments, demographic modelling or handling large datasets as well as holding a valid driver's license is a plus. Application / Contact Please upload your application via our online
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different methods such as modeling mass flows analysis (MFA), Life cycle analysis (LCA) and semi-quantitative methods for decision support for sustainable innovation. PhD Student in Safe and Sustainable Green
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, if any, must be included) – Certified copy of Academic Degree/s in original language along with a certified translation into English, and/or Diploma Supplement (if applicable) – Certified copies
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reinforcement learning for large language models (LLMs). Research directions include developing next-generation post-training algorithms, exploring diffusion-based approaches to reasoning with language models
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. Empa is a research institution of the ETH Domain. Empa's Laboratory of Biomimetic Membranes and Textiles is a pioneer in physics-based modeling at multiple scales. We bridge the virtual to the real world
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modification of primary human immune cells (T cells and macrophages). Conduct in vitro validations using advanced models, including patient-derived organoids and co-culture systems. Perform in vivo validations