PhD-holding research assistant in the field of chemistry, physics, materials science or a comparable field (m/f/d)

Up and extension and cooperation in the standardization of meta and research data management with the help of ELN/LIMS systems and ontologies/knowledge graphs Development, standardization and maintenance of the code for hardware control, orchestration and data management of the self-driving labs/materials Acceleration Platforms (SDLs/MAPs) developed in the field Integration of new hardware and software tools into the SDL/MAP technology stack Application and implementation of machine learning and artificial intelligence methods related to active learning for feedback loops in MAPs/SDLs Development of ideas and research concepts, production of applications and third-party funding Coordination and processing of research and development projects on data management and SDLs/MAPs (e.g. focusing on workflows, ELN/LIMs connection, backend and frontend in SDLs/MAPs) Care and technical guidance of young scientists Development of reports, posters, project reports, etc. Publication of results in international journals and presentation at national and international level Cooperation with internal and external experts on Self-Driving Labs, Materials Acceleration Platforms, Ontologies, Knowledge Graphics, Data Management, ELN/LIMS Active cooperation in the field of material and specific in the fields of activity Advanced Materials and Material Design Active cooperation in MAPs@BAM to support laboratory automation

Successfully completed academic studies in the field of chemistry, physics, computer science, material science or a comparable subject with very good doctorate in a related subject area Comprehensive knowledge and practical experience in dealing with and creating knowledge graphs and ontologies (e.g. project MaterialDigital) (covered by publications or software contributions) Experience in the field of implementation and hardware integration for laboratory automation or high throughput methods (based on successfully completed modules or publications) Very good programming knowledge (especially in Python) (covered by published software contributions or publications) Knowledge and practical experience with research data management and ELN/LIMS systems (based on successfully completed modules, publications or software contributions) Experiences in the field of machine learning and artificial intelligence (e.g. Bayesian Optimization, Neural Networks) as well as code management tools (e.g. GitHub) are provided, also confirmed by publications or software contributions Experience in the acquisition and management of R&E projects and corresponding scientific project management Experience in the care of young scientists Extensive publication experience (more than four publications with first or last authorship) Very good English skills in word and writing German knowledge is advantageous Good communication and information behaviour, targeted and structured working, initiative/willingness to use and skills, team capacity and willingness to cooperate, readiness to learn

30 days for a 5-day week Work in national and international networks with universities, research institutions and industrial companies Attractive and modern work environment with excellent infrastructure and equipment at the latest scientific level (Labore, BAM Data Store, High-Performance Computing etc...). Occupational pension (VBL) Possibility of mobile work up to 60% Job Ticket Grant We offer: Access to high-quality training offers and training on digitalization topics such as programming, research data management, AI applications and machine computing in science. Networking and exchange in our interdisciplinary digitization communities as well as BAM digitalization events to develop innovative solutions together with our experts. Targeted competence acquisition in key technologies of laboratory digitization – modern programming paradigms, connection to the electronic laboratory book and development of metadata formats and ontologies – through practical learning formats such as workshops, online courses and collaborative trainings with bundled knowledge sources. Possibility to actively co-design BAM digitalization projects and to introduce own ideas for digital innovations. .