Passion & Competence
AI4matter GbR offers comprehensive education and consulting services tailored specifically for material scientists looking to enhance their skills in the field of artificial intelligence. We provide in-depth training to help scientists and professionals harness the power of machine learning in their research and development projects. Our team of experienced specialists is dedicated to helping individuals and organizations stay ahead of the curve in this rapidly evolving field.

Beginner’s Course in Machine Learning
Together with our partner institution we aim to develop a training program focusing on machine learning tailored specifically for the needs of material scientists, a field where AI methods are often not yet part of the regular curriculum. With this course, we will provide participants with practical skills for advanced data analysis, material prediction and experimental design using AI. The training is available both online and (in part) in-person. This offers flexible participation for European learners and the possibility for networking locally with other material scientists interested in integrating deep tech methods into their analytical toolkit.
The teaching and subsequent challenge will be conducted by an interdisciplinary team of experts experienced in academic teaching and professional training from the fields of machine learning, computational materials science, computer and data science, measurement techniques and image processing.

The Team

Dr. Silvia Vock	Dr. Franziska Wolny	Dr. Weijia Shao	Arne Sonnenburg
Materials Science Image Processing Magnetism	Physics Material Analysis Semiconductors Data Science	Computer Engineering Machine Learning Software Development	Mechatronic Image Processing Probabilistic Filters

Silvia Vock studied materials science at TU Dresden, specializing in computational materials science and functional materials. In her doctorate, she focused on quantitative magnetic force microscopy and the resolution of micro-magnetic structures in thin films, nano wires and superconductors. She then worked in the field of additive manufacturing at Fraunhofer, where she first came into contact with machine learning methods for predicting component properties. In 2022, she helped set up a research team focusing on AI reliability and safety in the context of occupational health and safety, which she has led ever since.   
After accepting an invitation to give an ML tutorial at an IEEE Magentism conference at the end of 2023, she felt the need to find a way to reconnect with her original professional background in her daily work.

Franziska Wolny studied applied natural science with focus on semiconductor physics in Freiberg. She then got a PhD in experimental physics at IFW Dresden investigating magnetic properties of iron filled carbon nanotubes. After 10 years of experience as R&D senior scientist in the semiconductor industry, she shifted her work and research interest towards data science and machine learning. She is part of Silvias AI team since 2023 and focuses on risk assessment of AI in machinery.

Weijia Shao studied computer science at Saarland University and received his doctorate with a dissertation on optimisation methods for machine learning. From January 2013 to September 2023, Weijia worked as a research assistant at the Distributed Artificial Intelligence Laboratory (DAI Laboratory) of Faculty IV (Electrical Engineering and Computer Science) at Technische Universität Berlin (TU Berlin). As a member of the Artificial Intelligence & Machine Learning (AIM) Competence Centre of the DAI Laboratory, Weijia was involved in national and international research projects in the field of machine learning, which were carried out in collaboration with various industrial, academic and public partners. Since 2023 Weijia joined Silvias AI team as postdoctoral researcher.
Weijia loves teaching and is also interested in gaining further experience in the consulting field as part of his post-doctoral career.

Arne Sonnenburg studied mechatronics at the TU Dresden. As research assistant he focused on algorithms for image-based rendezvous navigation between spacecrafts. In this time he also created an inter-institutional advanced course on mobile robotics. Until 2021 he was a development engineer in the "Image Processing" group at XENON in Dresden. Since 2021 he is a research associate at BAuA.
In his career, he has repeatedly come into contact with topics that are indirectly or directly related to image processing and AI (probabilistic filters, image data management, AI-based image processing algorithms, AI for machine safety). He has a great affinity for knowledge transfer: creating a robotics club with promotion of young talent as a student, teaching as a university employee, development of new technologies and methods and passing them on to colleagues at XENON, frequent lectures on AI regulation.
Arne finds the potential of AI super exciting, especially when considering what is possible compared to classic image processing. He also loves being able to pass on knowledge,  especially when this helps others to recognize so many new possibilities.