-
learning, particularly deep learning and physics-informed methods, offer transformative opportunities to redesign how data are acquired and reconstructed, and how physiological parameters are inferred from
-
. 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
-
educates the next generation of structural engineers, equipping them with deep technical knowledge and top-level competencies in the use of timber as a high-quality building material, contributing
-
Master’s degree in computer science, electrical engineering, or related discipline. Strong background in machine learning, deep learning, and optimization. Interest in cross-domain research linking ML