53 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" positions at Forschungszentrum Jülich
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
Your Job: Investigate current challenges and bottlenecks in power flow analysis for large scale electrical distribution grids Apply machine learning/AI or surrogate modeling (e.g., neural networks
-
a soldering station, hardware tools, as well as using a computer You are willing to expand your knowledge to cover the entire range of sample environment at JCNS You are a good team-player when it
-
datasets with machine learning methods, and software development are beneficial Good organisational skills and ability to work systematically, independently and collaboratively Effective communication skills
-
Your Job: Machine Learning (ML) and artificial intelligence (AI) based on neural networks are currently reshaping all aspects of society. In several areas, such as medicine, AI-based tools
-
Profile: A Master`s degree and an excellent PhD degree in Biochemistry, Chemistry, or a related Molecular Science Proven Track Record in Machine Learning, Molecular Simulations, Chemoinformatics
-
, and training methods - across multiple technological platforms - photonics, electronics, biological neurons. Responsibilities and tasks This PhD project aims to develop, verify, and benchmark learning
-
-learning–based segmentation, species classification and lineage tracking workflows for multi-species time-lapse data Optimise models and pipelines for real-time performance, enabling adaptive imaging and
-
Your Job: In this position, you will be an active member of the SDL “Fluids & Solids Engineering” and will collaborate strongly with the SDL “Applied Machine Learning”. You will have the following
-
twin of sperm motility, and utilize it to develop a separation method. Your tasks will include: Performing computer simulations and matching them to experimental data Very close collaboration with
-
Your Job: We are looking for a PhD student to contribute to the development of fast, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular