Sort by
Refine Your Search
-
, storage, and local electricity grids. A key goal is to translate methodological innovations in deep learning into practical tools for sustainable urban energy systems, supporting applications in forecasting
-
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
-
cellular reprogramming strategies and identify new omics-based biomarkers. We work closely with clinical partners and we focus on deep understanding of molecular mechanisms of disease development. PhD
-
at the interface of machine learning, statistics, and live-cell biology. The position is co-supervised by Prof. Olivier Pertz (Cell Biology) and Prof. David Ginsbourger (Statistics), and the student will be equally
-
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
-
. The PhD position will focus on developing a deep-learning algorithm for analyzing the acquired experimental data. The PhD position will focus on development a comprehensive and AI-driven platform
-
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
-
models of protein structures and complexes for application in life sciences. In addition, we develop and maintain PLINDER, a resource designed to drive breakthroughs in deep learning-based protein-ligand