We are looking for a postdoctoral researcher with a strong expertise in both materials science and data analytics to consult and work with BiGmax scientists at the various participating Max Planck Institutes on applying machine learning and data mining to their various cutting-edge materials science problems.

BiGmax is a network of the Max Planck Society (https://www.mpg.de/en) aiming at exploiting the potentials of data-driven materials science. The BiGmax scientists from more than 10 different Max Planck institutions cover a significant breadth in research areas and the envisioned synergy will enable them and their institutes to develop novel, domain-specific and property-specific methods. More about BiGmax can be found here: https://bigmax.iwww.mpg.de/ (under development)

Job description: You will advise and consult BiGmax scientists (newcomers as well as all participating groups) on the various flavors of machine learning and data mining (e.g. random forest, kernel ridge regression, support vector machines, neural networks, compressed sensing, and more) with regards to their specific research problems. Thus, your work environment will be multifaceted and you will get the opportunity to work on various different exciting cutting edge research problems. This will also enable you to identify possible unused synergies inside the BiGmax network. Consequentially, bringing together potential collaboration partners will also be considered as one of your work tasks. Additionally the coordination of a central analytics toolbox serving the various BiGmax projects will be part of your responsibilities.

Requirements: You should have a strong background in both, data analytics and materials science; ideally, you have already applied data-analytics models to relevant materials science problems. You should possess a strong ability for organizing scientific collaborations.

Depending on specialization, the position will be at one of the participating Max Planck Institutes. You will receive a contract (TVöD) for two years. A prolongation is possible.

Please send your application material, including a motivation letter, CV, and contact-details of at least two references as one pdf-file to [email protected]. Review of applications will end when a suitable candidate is found.