Sort by
Refine Your Search
-
Listed
-
Employer
-
Field
-
experience in machine learning and data-driven modeling. Ability to work independently as well as collaboratively in an interdisciplinary team. Excellent command of English, both written and spoken. Previous
-
: Molecular Thermodynamics Modeling for the Energy Transition The upcoming Molecular Engineering Thermodynamics (MET) Group at ETH Zürich is looking for a doctoral student to develop and improve computational
-
-climate interactions. For this purpose, it has developed its own dedicated global model SOCOL, which can interactively treat all processes and major feedbacks related to the ozone layer and atmospheric
-
PhD student position on Optical Control over Topological Phases of Matter (ERC Starting Grant) Starting April 2026 (or as agreed) The newly-established “Quantum Opto-Electronics” research group
-
Starting Grant project, which aims at controlling strongly correlated topological phases of matter with light. The successful candidate will become an essential member of our fast-growing research team
-
, signal in-tegrity) under varying physiological and environmental conditions Conduct statistical evaluation and model validation using controlled measurements and sensing-dummy reference data Collaborate
-
the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real
-
more cost-efficient. Together, UESL and IMOS are seeking a motivated and qualified PhD candidate to advance the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By
-
control of biological systems This project provides a rare opportunity to see mathematical models come alive, guiding experiments as they happen. Teaching: The position includes a small teaching duty in
-
transduction schemes that deliver high sensitivity and selectivity in complex media Validation in controlled in vitro and ex vivo systems, enabling quantitative correlations between electrochemical signals and