DGM-Tag 2024: The award winners introduce themselves - DGM Young Talent Prize - Dr.-Ing. Ruben Wagner

With the DGM Young Talent Prize we honor outstanding young scientists whose scientific work shows or is expected to show above-average results and whose work is related to committees or events of the German Society for Materials Science. This year we are pleased to present the award to several PhD students and PhDs at the DGM-Tag 2024.

We are pleased to announce the DGM Young Talent Prize 2024, which recognizes outstanding Ph.D. students who have graduated no more than two years ago at the time of application. This prestigious award is dedicated to young scientists who have made an outstanding contribution to non-profit research in the field of materials science and engineering. The DGM congratulates Dr.-Ing. Ruben Wagner, GfE Fremat GmbH, on winning the DGM Young Talent Prize 2024.

1) Dr. Wagner, what does the DGM Young Talent Prize mean to you and what role has the German Society for Materials Science played in your professional development so far?

I feel that the DGM Young Talent Prize is a great recognition of my scientific work. As a young scientist, I often regretted that personal scientific exchange was more difficult in the era of predominantly digital conferences. With its events - onsite, digital or hybrid - DGM helped me to share my results with an interested audience.

2) You have been involved in the "Science Meets School - Materials and Technologies" school lab and have inspired students about additive manufacturing. What motivates you to pass on your enthusiasm for science to the next generation?

What I enjoyed most about the school lab was explaining scientific relationships to the girls and boys in simple terms and showing them in everyday life. Additive manufacturing was particularly suitable for this, as the students could determine everything themselves, from design to production to characterization. I was often surprised by the creativity of the students in the workshops.

3) You have independently developed an artificial intelligence evaluation routine for computed tomography images. What approaches and methods did you use and how did you involve your team in the process?

A colleague encouraged me to use deep learning algorithms to evaluate the different phases and the fatigue crack in an aluminum alloy. The methods I used included ideas for evaluation routines from biomedicine. To my surprise, the transfer to materials science was more successful than expected. Within the institute, we then checked the plausibility of the results as a team and found them to be good

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