Transforming the World
MATH+ funds six positions for heads of independent junior research groups in application-driven basic mathematical research, in order to strengthen support for excellent young researchers who already have some years of postdoc experience, and to give them a head start in building their own research profile and group within the supportive environment of a large Cluster of Excellence.
The current group leaders are:
Application-driven Random and Multiscale Dynamics
Maximilian Engel took up his position as head of the MATH+ Junior Research Group “Application-
driven random and multiscale dynamical systems” at FU Berlin in July 2020. His research concerns various areas within Dynamical Systems, including random dynamical systems and their parameter-dependent change (bifurcations) as well as fast-slow ODEs, SDEs and PDEs, in particular those with non-hyperbolic singularities. Additionally, he is working on stochastic models in population dynamics, with an increased focus on game theory and (chemical) reaction networks.
Maximilian Engel studied mathematics, economics, and philosophy at LMU Munich, Munich School of Philosophy, and Imperial College London. He received his Ph.D. in mathematics from Imperial College London in February 2018, under the supervision of Jeroen Lamb and Martin Rasmussen. Afterward, he held a Postdoc position at TU Munich within the DFG CRC 109 “Discretization in Geometry and Dynamics”, working with Christian Kuehn.
Mathematical Foundations of Data Science
Nicole Mücke took up her position as head of the MATH+ Junior Research Group “Mathematical
Foundations of Data Science” at TU Berlin in March 2020.
She is currently working in the field of statistical learning theory, in particular, deep learning, the efficiency of kernel methods, stochastic approximation methods, and statistical inverse problems.
Nicole Mücke studied mathematics both in Potsdam and at HU Berlin. She received her Ph.D. from the University of Potsdam in October 2017. Afterward, she held postdoc positions at the Istituto Italiano di Tecnologia Center for Convergent Technologies for one year, and for another year at the University of Stuttgart before she returned to Berlin.
Statistical Inversion and Quantification of Uncertainties
Fabian Telschow took up his position as head of the MATH+ Junior Research Group “Statistical Inversion and Quantification of Uncertainties” at HU Berlin in March 2021.
In his research he developes statistical methodologies for image analysis and functional data using random field theory. His focus lies on inferential methodologies such as confidence sets for random fields on irregular domains in arbitrary dimension and related topics such as quantification of stochastic uncertainties of level sets. With the group of Thomas Nichols (University of Oxford) he applies these methods to neuro-imaging data. Further applications comprise for example biomedical gait data and climate data.
Fabian Telschow studied mathematics and physics at the Georg-August-University Göttingen and received his Ph.D. in mathematical statistics from Georg-August-University Göttingen in September 2016, under supervision of Stephan Huckemann and Axel Munk. Afterward, he held a Postdoc position at University of California, San Diego, working with Armin Schwartzman.