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the data. Job description This PhD project focuses on developing learning-based strategies for accelerated data acquisition, physics-informed image reconstruction, and quantitative inference
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. Project background We are excited to announce an interdisciplinary PhD opportunity focused on mechanochemical processes driving radical formation and redox cycling in the deep subsurface, with implications
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the group of Prof. Roman Stocker, and focuses on the development and deployment of a new‑generation underwater imaging device to transform drift studies in riverine ecosystems. It involves a collaboration
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and setup: Within the project, we follow a multidisciplinary collaborative approach for which we are have recruited 3 PhD students focusing on material science, advanced in vivo imaging and computation
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to imaging with geophysical data. Our research focuses on mathematical methods for processing, imaging, and inversion of geophysical data, the physics of wave propagation, and the development of innovative
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nanometers to tissues, to regulate collective cellular behavior. Mechanobiology seeks to understand how cells, tissues, and organoids perceive, process, and remodel mechanical signals, and how these processes
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actuation for soft continuum robots Sensing and tracking of minimally invasive robotic systems Autonomous control in uncertain anatomical environments Computer vision for image-guided robotic procedures (incl
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Systems We have an open PhD position at the intersection of machine learning, embedded intelligence and human–computer interaction. The project will explore how learning systems can become more adaptive