-
for practical medical use. This project aims to create accurate and rapid surrogate models by combining physics-aware learning methods with domain decomposition techniques, enabling parallel training and
-
19 Jan 2026 Job Information Organisation/Company Forschungszentrum Jülich Research Field All Researcher Profile First Stage Researcher (R1) Application Deadline 19 Jan 2038 - 03:14 (UTC) Country Germany Type of Contract To be defined Job Status Other Is the job funded through the EU Research...
-
-aware learning methods with domain decomposition techniques, enabling parallel training and efficient GPU-supported implementation. Your tasks: Development of physics-aware ML models for 3D blood-flow
-
Your Job: This PhD project bridges between classical analytical methods and modern AI based techniques to analyse spike train recordings to advance our understanding of neural population coding while maintaining clarity in the interpretation of results. Concurrently, AI-based methods are...
-
strong background in applied mathematics Excellent programming skills (Python, C/C++) Good experience in machine learning and parallel computing Good organisational skills and ability to work both
-
the experimental data and the concepts of neuronal coding, and Elephant Analysis of the parallel rate data for submanifolds and their temporal dynamics during behavior Leverage dimensionality reduction and
-
experience in machine learning and parallel computing Good organisational skills and ability to work both independently and collaboratively Experience with deep learning frameworks, such as Tensorflow
-
related in space and time and to behavioral events. Core Tasks: Getting familiar with the experimental data and the concepts of neuronal coding, and Elephant Analysis of the parallel rate data for