Unsere Mitarbeiter*innenFoto: Manuel Gutjahr
- Hempel, S.; Adolphs, J.; Landwehr, N.; Janke, D.; Amon, T. (2020): How the selection of training data and modeling approach affects the estimation of ammonia emissions from a naturally ventilated dairy barn - Classical statistics versus machine learning. Sustainability. (3): p. 1030. Online: https://doi.org/10.3390/su12031030
- Hempel, S.; Adolphs, J.; Landwehr, N.; Willink, D.; Janke, D.; Amon, T. (2020): Supervised Machine Learning to Assess Methane Emissions of a Dairy Building with Natural Ventilation. Applied Sciences. (19): p. 6938. Online: https://doi.org/10.3390/app10196938
- Chaillet, M.; Lengauer, F.; Adolphs, J.; Müh, F.; Fokas, A.; Cole, D.; Chin, A.; Renger, T. (2020): Static Disorder in Excitation Energies of the Fenna-Matthews-Olson Protein: Structure-Based Theory Meets Experiment. Journal of Physical Chemistry Letters. : p. 10306-10314. Online: https://doi.org/10.1021/acs.jpclett.0c03123
Julian Adolphs is a postdoc in the junior research group data science in agriculture at ATB. He received his PhD in 2008 at the Free University Berlin in theoretical physics. After his PhD he worked in the scientific service of the German parliament for 15 months. After this excursion to scientific policy advice he returned to science and worked as a researcher in the division of epidemiology, etatistics and eathematical eodelling at the federal institute of risk assessment (BfR) in Berlin for two years. There he developed probabilistic and toxicokinetic models for the accumulation of persistent organic pollutants in food. From 2013 to 2019 he worked as a university assistant in research and teaching at the department of theoretical physics at Johannes Kepler University Linz (Austria). In Linz he also found his interest in data science and machine learning. Based on his experience in mathematics, statistic and programming, he is now applying machine learning/deep learning methods to agricultural problems.