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Research Assistant (postdoc)

Freie Universität Berlin

The reconstruction of discretized geometric shapes from empirical data, especially from image data, is important for many applications in medicine, biology, materials science, and other fields. During the last years, a number of techniques for performing such geometrical reconstructions and for conducting shape analysis have been developed. An important mathematical concept in this context are shape spaces. These are high-dimensional quotient manifolds with Riemannian structure, whose points represent geometrical shapes. Using suitable metrics and PDFs on such manifolds, distances between shapes or statistical shape priors (for utilization in reconstruction tasks) can be defined. A frequently encountered situation is that instead of a set of discrete shapes a series of shapes is given, varying with some parameter (e.g. time). The corresponding mathematical object is a trajectory in shape space. For many analysis questions it is helpful to consider the shape trajectories as such (instead of individual shapes) - often together with co-varying parameters.

Job description:

In this project, you will work with Prof. H.C. Hege, Prof. Dr. T.J. Sullivan, and Dr. C. von Tycowicz conducting research in shape analysis with a focus on the development of new mathematical methods for the analysis, processing and reconstruction of empirically defined shape trajectories. The applicant will develop and implement algorithms to enable the analysis of data from medical collaboration partners, especially from cardiology where a detailed understanding of the typical types of shape deformations will be a valuable tool for the evaluation and quantification of heart diseases. The position is embedded into the Einstein Center for Mathematics Berlin (ECMath) and the applicant is expected to collaborate closely with the cooperation partners from ECMath.

Requirements:

University degree in mathematics, computer science, or related disciplines

Theoretical or practical knowledge regarding optimization algorithms

sound software development skills (Java, C++, Python or a comparable language)

Applicants are expected to be highly motivated, self-reliant, and interested to collaborate with scientists in medicine

good command of written and spoken English is essential.

Desirable:

Desirable characteristics include familiarity with at least two of the following fields: geometry processing, computer vision, differential geometry and numerical analysis

theoretical or practical knowledge regarding optimization algorithms

sound software development skills (Java, C++, Python or a comparable language)

Applicants are expected to be highly motivated, self-reliant, and interested to collaborate with scientists in medicine

good command of written and spoken English is essential.

Since the shape trajectories of interest will have to be inferred from imperfect data sources, applications from candidates with additional experience in computational statistics will be especially well received.

 

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