Dominik Grimm

Prof. Dr. Dominik Grimm is a Professor in the Department of Forestry and Forestry Management at the University of Applied Sciences Straubing (HSWT). He leads the Straubing site for sustainable resource use and is affiliated with the Faculty of Forestry and Forest Management. His research focuses on interdisciplinary applications of machine learning, computational biology, and optimization in agriculture, biotechnology, and environmental science. Key areas include precision agriculture (e.g., UAV-based weed detection in sorghum fields), protein engineering (thermostability prediction), and reinforcement learning for process synthesis. He has pioneered tools like permGWAS2 for genome-wide association studies and EVARS-GPR for seasonal time series forecasting. His work integrates AI with real-world challenges, such as optimizing hospital bed management and improving crop yields through data-driven strategies.

• Education: Advanced degrees in computer science and bioinformatics (details not explicitly stated).

Research interests span computational methods in agriculture, genomics, and combinatorial optimization. He supervises doctoral candidates like Josef Eiglsperger and Jonathan Pirnay. His lab (Grimmlab) develops open-source frameworks for time series analysis and phenotypic data processing. Key projects include automated process design using reinforcement learning and machine learning for perishable product forecasting in horticulture.

Publications highlight contributions to protein stability modeling, agricultural robotics, and data stream algorithms. Collaborations involve institutions like the University of Bayreuth and the Helmholtz Center Munich. His work emphasizes practical applications, such as reducing herbicide use through precision agriculture and enhancing biomanufacturing via AI-driven enzyme discovery.

Grants and research projects funded include methods for flowsheet synthesis, generative AI in enzyme discovery, and sustainable resource management. He leads initiatives integrating machine learning with horticultural sales prediction and patient bed assignment optimization in hospitals.