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this. It is important to develop a detailed picture of the cell types affected by these changes and if possible the temporal sequence in which they occur in order to intervene to restrict cancer progression
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hybrid models that integrate limnological knowledge into machine learning models following the paradigm of Knowledge-Guided Machine Learning (KGML). The position is part of an on-going project
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. Nature Physics20, 970 (2024)). You will also work on expanding our coherent imaging methodology to look at dynamics and phase switching in materials at the nanoscale (Johnson et al. Nature Physics19, 215
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prototype testing and field demonstrations. While working within these application domains, you will have freedom to identify research questions, propose novel approaches, and pursue innovative solutions. We