AI Consulting

The AI consultant team supports scientists from all domains in applying AI methods for their scientific research questions. We are very welcome to not only discuss your questions but also support in the implementation.

In the last decades the field of Artificial Intelligence (AI), or more precisely Machine Learning (ML) has seen an unprecedented raise of attention and development speed. The AI consultant team at FZ Jülich support scientists from various domains in the application of AI methods and keeping up with the pace of developments. The support ranges from discussing first steps in formulating research questions to technical details and cutting edge developments.

Located at the Jülich Supercomputing Center, we are especially interested in bringing AI applications to large scale supercomputers. Recent advances have shown that using large-scale data, for example for pre-training, can lead to significantly enhanced performance and/or better data efficiency in deep learning models. Combining AI and High Performance Computers (HPC) means not only code parallelization and optimization but, in many cases, also algorithmic adjustments. Therefore, HPC experts with focus on AI are always also AI experts.

We are at the interface between researchers from various fields and machine learning. Therefore, we are very interested in combining ML methods with domain-specific approaches and/or simulations. We firmly believe that using elements of classical theories such as symmetries can strongly boost the quality of ML methods. Progress in the ML field can also greatly improve classical approaches, for example when using differentiable simulations.

The team support the JSC infrastructure in several ways. We maintain the installations of all relevant ML software stacks and make sure that the usage of the supercomputers for ML is smooth. Furthermore, we support HAICORE compute resources where we make sure that Helmholtz association researchers can use JSC's resources for AI-related activities. Our work can be considered part of the support infrastructure as well.

Within Helmholtz AI, researchers can easily apply for our collaboration and support using the Voucher submission system. We highly recommend discussing with us first. Please contact a team member or our central mail address consultant-helmholtz.ai@fz-juelich.de.

Team members

Prof. Dr. Stefan KesselheimHead of SDL Applied Machine Learning & AI Consultant team PI in Helmholtz Information Program 1, Topic 1Building 14.14 / Room 3023+49 2461/61-85927

Dr. Alexandre StrubeBuilding 16.3v / Room 3001/3002+49 2461/61-3866

Alina BazarovaBuilding 14.14 / Room 3002+49 2461/61-1234

Sabrina BenassouBuilding 14.14 / Room 3002+49 2461/61-85383

Jan EbertBuilding 14.14 / Room 3002+49 2461/61-96550

Projects

Project SunGAN

In the Project "Distributed GANs for synthetic Solar images" we train Generative Adversarial Networks (GANs) that create realistic images of the sun in extreme ultraviolet.

MLWater - Delta Learning of water potential energy surfaces

In the Project MLWater we explore different techniques for Learning a Neural Network Potential that corrects a classical water model to obtain accurate potential energy surfaces for water molecules in bulk and at interfaces.

CovidNetX Initiative

In the CovidNetX Intiative, we contribute to the global fight against the COVID pandemic by developing an medical imaging technique based on large scale transfer learning.

Understanding Spin-Tune Variations at COSY

In the project "Understanding Spin-Tune Variations at COSY" we help understanding interesting measurements of the "Spin Tune" with Machine Learning methods at the particle accelerator COSY in Jülich.

AlphaNumerics Zero

In the project AlphaNumerics Zero, we work on Reinforcement Learning methods that accelerate the convergence of a numerics method.

Jülich Challenges

In the Jülich Challenges project, we support the creation, deployment and lauch of a data challenges website for Forschungszentrum Jülich.