Research assistant (m/f/d) in the field of civil engineering, mechanicalengineering, physical engineering, applied physics, mathematics, computerscience or comparableSalary group 13 TVöDTemporary contract for 3 yearsunder the reserve that funds are grantedFull-time / suitable as part-time employmentThe Bundesanstalt für Materialforschung und -prüfung (BAM) is a materialsresearch organization in Germany. Our mission is to ensure safety intechnology and chemistry. We perform research and testing in materialsscience, materials engineering and chemistry to improve the safety of productsand processes. At BAM we do research that matters. Our work covers a broadarray of topics in the focus areas of energy, infrastructure, environment,materials, and analytical sciences.We are looking for talented people to join us.Your responsibilities include:Processing of a DFG project within the SPP Hundert plus - Extending theservice life of complex building structures through intelligentdigitalization on the topic of combining FE simulations with machine learningin the field of bridge monitoring.Assessing the reliability of structures and prioritizing maintenance measures,especially for bridges, is a major challenge for Germany's infrastructureoperators. This often involves the use of simulation models whose forecastquality can be specifically improved by measurement and monitoring data. Aspart of the priority program Hundert plus - Extending the service life ofcomplex building structures through intelligent digitalization, methods willbe developed in various subprojects to optimize this process and validate iton a central demonstrator. 

Within the subproject Data-driven model adaptation for the identification ofstochastic digital twins for bridges, methods for the development of adigital twin will be developed.• Development of stochastic procedures to determine the model parameters ofFEM models• Development of metamodels (Gaussian processes and physics informed neuralnetworks)• Data-based identification of required model adjustments• Integration of the procedures for the demonstratorYour qualifications:• Completed scientific university studies (diploma or master's degree) incivil engineering, mechanical engineering, physical engineering, appliedphysics, mathematics, computer science or a comparable discipline• Proven knowledge and experience in the development of FEM applications(e.g. FEniCS)• Basic knowledge in the area of Bayesian statistics• Basic knowledge in the field of mechanics• Experience in the field of machine learning is an advantage• Fun with programming (Python)• Good knowledge of the German and English language, both written and spoken• Good communication and information behaviour as well as goal-oriented andstructured way of working• Initiative/ commitment, ability to work in a Team/ willingness tocooperate as well es willingness to learnWe offer:• Interdisciplinary research at the interface of politics, economics andsociety• Work in national and international networks with universities, researchinstitutes and industrial companies• Outstanding facilities and infrastructure• Flexible working hours and mobile working