Sort by
Refine Your Search
-
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...
-
Do students receive financial aid? All doctoral positions are fully funded, including social benefits. Students also receive funding to attend conferences and other events related to their research, and have access to outstanding facilities. Do I need to know English? Yes, English is the...
-
. Your tasks: Development and comparison of data driven models for the prediction of stresses in arterial walls and plaque Enhancing the models with physics, i.e., using different physics-aware machine
-
arterial walls and plaque Enhancing the models with physics, i.e., using different physics-aware machine learning models from the field of scientific machine learning Exploiting large language models
-
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
-
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
-
algorithms Extend the superstructure to tackle AC-PF problems of different complexities and assess its convergence in inference Investigate scaling and performance bottlenecks Explore hybrid ML-classical