The Berlin Mathematics Research Center MATH+ sets out to advance mathematics itself and its interdisciplinary power with the aim of achieving progress on grand challenges in a wide variety of application fields.
MATH+ will start with nine major units for project-oriented research: four Application Areas and five Emerging Fields. In both, mathematicians from a wide range of different disciplines collaborate – with each other and with leading researchers from diverse application fields as well as representatives from industry. These Research Units are complemented by Transfer Units designed for translational research. For further details please visit www.mathplus.de.
In the frame of the Cluster of Excellence, MATH+ project EF4-4 “Diffusion in dynamically crowded cells: stochastic models from spatiotemporal motion data” is looking for a research assistant. Diffusion in cellular environments is a multiscale process that involves a variety of interacting particles and molecular species. The project is devoted to the stochastic modelling and the statistical analysis of spatiotemporal transport, based on data from fluorescence experiments and molecular simulations. Main objectives are the inference of effective stochastic models and the quantification of memory effects, using state-of-the art techniques from multiscale modelling, complex analysis, and data assimilation. It is expected that the successful applicant will contribute to both theoretical and numerical tasks of the project. In turn, we offer an international and interdisciplinary research environment that facilitates active research collaborations within the MATH+ cluster (and beyond) and that provides an excellent environment for the candidate‘s academic development.
MSc. or equivalent in mathematics, physics, or related fields.
We are searching for a talented and motivated scientist with proven expertise in at least one of the following subjects: stochastic differential equations, multiscale analysis, anomalous transport, molecular dynamics, or statistical inference. The applicant should have a solid background in applied mathematics or theoretical physics; relevant experience in the implementation and testing of numerical algorithms is expected. The applicant should be open towards interdisciplinary cooperations (e.g., life sciences or financial mathematics) and be able to play an active role in international collaborations.