11 machine-learning-phd positions at Forschungszentrum Jülich in Germany

PhD-position - Data Mining, Inference, Prediction and Simulation in Semiconductor material

Infrastructure? No Offer Description Work group: IAS-9 - Materials Data Science and Informatics Area of research: Promotion Job description: Your Job: You will strengthen the data science and machine learning

PhD Position – Co-Design of Neuromorphic Algorithms and Circuits

non-von Neumann computing hardware blocks for AI, optimization, probabilistic computing, and scientific computing. Machine learning, deep learning and algorithm development, co-designing with mixed

PhD Position – Co-Design of Neuromorphic Algorithms and Circuits

scientific computing. Machine learning, deep learning and algorithm development, co-designing with mixed analog-digital circuit blocks Associative computing from both a circuits and algorithms perspective

PhD Position in AI with HPC - Advancing Earth Observation through Foundation Models

Infrastructure? No Offer Description Work group: JSC - Jülich Supercomputing Centre Area of research: Promotion Job description: Your Job: You will join the Simulation and Data Lab `AI and Machine Learning

Postdoc – Molecular and bioinformatic-based identification of optimized enzymes for biotechnological applications

to identify degrading enzymes from different data resources Using Hidden Markov Models and similar tools as well as machine learning for the identification of novel and better enzyme variants from a

PhD-position (m/w/d) – Computer Vision for In Situ Electron Microscopy

Your Job: You will strengthen the data science and machine learning activities of the IAS-9 with exciting new topics. You will work with a multidisciplinary team of enthusiastic data scientists

Postdoc / Research Associate - Machine Learning and Computational Fluid Dynamics

setups Conducting numerical predictions to assist in the design and optimization of SOC stacks/systems Supervising (PhD) students involved in related topics Summarizing and publishing results in scientific

Postdoc / Research Associate - Machine Learning and Computational Fluid Dynamics

potentially supporting experimental setups Conducting numerical predictions to assist in the design and optimization of SOC stacks/systems Supervising (PhD) students involved in related topics Summarizing and

Postdoc / Group Leader – Neutron Scattering in Energy Materials

on the atomic and mesoscopic scale using neutron methods, complementary X-ray experiments and support of further techniques including computer simulations Synthesis and physicochemical characterization of energy

Postdoc - ML-Based Image Processing

the efficient and reliable analysis and interpretation of different experimental imaging techniques such as atomic force or electron microscopy as well as tomography. We intend to use different machine learning