Calls
Ph.D. position in Numerical Analysis
The Ph.D. position is part of the IN-DEEP European Doctoral Network: "Real-time inversion using self-explainable Deep Learning driven by expert knowledge".
Topic: Adaptive strategies for explainable deep learning PDE solvers to speed up the generation of large and significant datasets.
The research program is a cutting-edge research activity mixing numerical methods for PDEs, deep learning and adaptive strategies for explainable PDE solvers. At the forefront of computational sciences, we are extending the approach to solving complex equations across diverse fields and investigating the reliability and efficiency of new deep-learning approaches.
Expertise in Python/C++, neural networks, and numerical methods will contribute to accelerating dataset generation and problem solutions. We are currently in search of passionate individuals who can demonstrate a high level of autonomy while also being eager to collaborate within a dynamic team environment. Instructions for application can be found at Euraxess.
Topic: Adaptive strategies for explainable deep learning PDE solvers to speed up the generation of large and significant datasets.
The research program is a cutting-edge research activity mixing numerical methods for PDEs, deep learning and adaptive strategies for explainable PDE solvers. At the forefront of computational sciences, we are extending the approach to solving complex equations across diverse fields and investigating the reliability and efficiency of new deep-learning approaches.
Expertise in Python/C++, neural networks, and numerical methods will contribute to accelerating dataset generation and problem solutions. We are currently in search of passionate individuals who can demonstrate a high level of autonomy while also being eager to collaborate within a dynamic team environment. Instructions for application can be found at Euraxess.