Sperrfristmeldungen
29.11.2024 21:18
Transfer of Science or Research, Advanced scientific education

Advancements in Materials Science: Unlocking the Potential of Deep Learning

Deep Learning - Fundamentals and Applications to Examples in Materials Science
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Deep Learning - Fundamentals and Applications to Examples in Materials Science | Download

In an era where technological innovation is driving industries forward, the integration of artificial intelligence (AI) and deep learning in materials science is revolutionizing the way materials are characterized, analyzed, and engineered. A forthcoming online training course, "Deep Learning - Fundamentals and Applications to Examples in Materials Science," aims to equip professionals with the knowledge and skills necessary to harness the power of deep learning in materials science applications.

Scheduled to take place from February 24 to 28, 2025, this comprehensive course will delve into the fundamentals of deep learning, convolutional neural networks, and their applications in materials science. Participants will gain hands-on experience with software tools such as PyTorch, FastAi, and Jupyter Notebook, as well as learn how to implement classification models, analyze tabular data, and efficiently classify image data.

The course is designed to cater to professionals in materials science, research and development, digitization, and AI, who seek to enhance their organization's AI capabilities and develop practical applications in materials science. By attending this course, participants will be able to:

Understand the principles of deep learning and its benefits in materials science
Apply deep learning techniques to specific materials science applications
Implement neural networks for classification and segmentation of image and tabular data
Discuss practical challenges and solutions with experts in the field
Led by renowned experts in the field, including Dr.-Ing. Dominik Britz and Dr.-Ing. Martin Müller from the Material Engineering Center Saarland (MECS), and Prof. Dr.-Ing. Frank Mücklich from Saarland University, this course promises to provide a unique opportunity for professionals to expand their knowledge and skills in deep learning and materials science.

Upon completion of the course, participants will receive a certificate, acknowledging their newfound expertise in deep learning and its applications in materials science. With the potential to significantly enhance their organization's technological capabilities and competitiveness, this course is an essential investment for professionals seeking to stay at the forefront of innovation in materials science.

Wissenschaftlicher Ansprechpartner:
Dr. Sebastian Slawik
Deutsche Gesellschaft für Materialkunde e. V.

Weitere Informationen:
https://dgm.de/akademie/en/deep-learning-fundamentals-and-applications-to-examples-in-materials-science-spring-training-course

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