Dietrich Buck

Om mig

Scientist with over a decade of research experience in AI, specialising in the application of machine learning to wood science and building technology. Actively integrates machine learning techniques into experimental research, data analysis, image processing, and simulations. These workflows extract insights from multidimensional datasets and transform diverse raw experimental data into interpretable structures that link physics-based models and computational intelligence.

Collaborates with academic and industry partners to extend machine learning beyond typical use cases. Approaches materials science, particularly building construction, from the perspective of experimental mechanics. Developed novel applications of measurement and analysis techniques in composite design and bio-based materials research, which provide validated workflows for full-field imaging, sensor fusion, data-driven material evaluation, and fluid–structure interactions.

Peer-reviewed publications resulting from collaborations with 19 universities plus research institutes in 13 countries reflect both pan-European and global engagement. Output reflects sustained productivity and creativity, deepening domain-specific knowledge and contributing to applied scientific progress.

Contributes to multi-modal mechanics assessment by refining full-field imaging techniques and applying data-driven methods for material evaluation. Full-field mechanics assessment applied in various formats, including static, quasi-static, cyclic, and high-speed experiments. Simultaneous measurements at different scales, from full-scale sections to tenths of millimetres, make it possible to differentiate finer material features with greater spatial and temporal resolution.

Experienced in leading multi‑institutional research projects, that bring together cross‑disciplinary teams and deliver validated outcomes. Holds independent responsibility for dedicated research, supervision, interdisciplinary development, and contributions to science policy through research evaluation. Born and raised in Sweden, near the Norwegian border. Native multilingual communication skills developed through sustained international collaboration and exposure to diverse linguistic environments.

Background and experiences are driving factors in applying machine learning across multiple dimensions, including scale-aware analysis, measurement uncertainty, hybrid data streams, and diverse experimental regimes. Committed to practical integration of data-driven insights in science and engineering through collaborative development.