-
machine learning-based software applications for materials science Develop code and utilize machine learning to support the automation of characterization and fabrication processes Ensure the integration
-
Your Job: You will strengthen the data science and machine learning activities of the IAS-9 with exciting new topics. You will work in a multidisciplinary team of enthusiastic data scientists
-
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
-
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
-
data collection and metadata extraction protocols to enhance machine learning-based software applications for materials science. Your Profile: Master’s degree in Engineering, Computer Science, Physics
-
for an enthusiastic researcher who is quick to grasp new concepts and ideas and can solve complex problems with high-quality software solutions. You will work with machine learning, HPC, and domain science experts and
-
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
-
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
-
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
-
, free energy calculations, machine learning/deep learning, Markov State Models, docking Your Profile: Master degree in the field of Biology/Physics/Chemistry or related High motivation and interest and/or