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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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on our previous work on DNA tracing technologies, we aim to develop tools and procedures to model and monitor the spread of pathogens without directly employing and measuring pathogens. Project background
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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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-mentored by two ETH professors from two different departments. If you don’t have hosts yet, Design++ can help match you based on your proposal. Project background You are a great fit if you’re excited to: (a
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and backtests to assess model performance and estimate the tool's real-world impact. You will have regular check-ins with the project team at Stanford but will conduct the day-to-day data work yourself
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with large language models (LLMs). The successful candidate will investigate both theoretical aspects – such as understanding the mechanisms and limitations of reasoning in modern LLMs – and practical
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responsibilities may include: Development or analysis of novel Machine Learning algorithms for engineering design applications, such as Inverse Design, Surrogate Modeling, or generative modeling. Collaborating with
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postdoctoral researcher, your responsibilities may include: Development or analysis of novel Machine Learning algorithms for engineering design applications, such as Inverse Design, Surrogate Modeling
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). Many experimental projects are motivated by theoretical questions, use highly quantitative techniques, and are integrated with theoretical modeling and analysis of the data. Successful candidates should
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(CPU/GPU), numerical modeling/Monte Carlo simulations are an asset Visualisation skills are an asset Careful way of working, checking of results Candidates can have an M.Sc. degree in STEM, or a Ph.D