Research Associate (wissenschaftlicher Mitarbeiter)

We offer two PhD positions within the Data Science in Hamburg Helmholtz Graduate School for the Structure of Matter (DASHH).

Further information on the school and on how to apply can be found here:

https://www.dashh.org

Our Projects 5 and 15 are entitled:

5. Phase Retrieval in Imaging and Speech Enhancement

Supervisors: Prof. Henry Chapman (DESY, CFEL, UHH, Fachbereich Physik), Prof. Timo Gerkmann (UHH, Fachbereich Informatik)

Abstract:

Advanced sources such as free-electron lasers produce intense and coherent beams of X-rays that are opening up new possibilities to image biological materials, such as single molecules, at atomic resolution. Since atomic-resolution lenses do not exist such methods usually rely upon retrieving the structural information encoded in the far-field coherent diffraction pattern [Shechtman et al, 2015]. This intensity pattern corresponds to the Fourier magnitude of the object, and is thus an incomplete measurement since the spectral phase cannot be measured. To reconstruct the original structure, the missing phase information needs to be retrieved . A very similar problem of phase retrieval is encountered in speech source separation and enhancement, which has received increasing attention recently [Gerkmann et al, 2015]. Just in the last few years, modern machine learning methods have been successfully applied to the problem aiming to obtain faster and more accurate results.
The goal of this project is to explore common concepts in X-ray imaging and speech enhancement. Recent advances in the application of machine learning in speech analysis will be transferred and tailored to X-ray imaging, obtaining deeper insights into this challenging inverse problem and providing new opportunities for high-resolution structure determination of biological systems.

15. Phase and Attenuation Retrieval for Propagation-Based Phase Contrast X-Ray Tomography

Supervisors: Prof. Dr. Martin Müller (HZG), Julian Moosmann (HZG), Prof. Dr.-Ing. Timo Gerkmann (UHH) 

Abstract:

Propagation-based phase contrast in near- and full-field X-ray imaging typically assumes the attenuation of X-rays transmitted by the sample to be negligible. However, most samples fulfill this condition only approximately due to residual absorption. Moreover, enhanced intensity contrast due to free-space propagation is beneficial for samples where absorption contrast is low or noisy or for samples which exhibit mixed contrast and where the soft tissue components require additional contrast enhancement such that their signal rises above the absorption signal of the surrounding material. Apart from certain approximations (small phase variations, small attenuation, small propagation distances, attenuation-phase duality), a direct algebraic inversion of the general problem as described by the Fresnel diffraction integral does not exist. For in situ or in vivo experiments, where deposited dose and/or scan time are critical, the acquisition of a second intensity measurement (at a different propagation distance or energy) is not feasible. For such experiments the inverse problem is ill-posed as phase and attenuation have to be reconstructed from a single measurement. The problem is related to phase retrieval in speech enhancement, where in the last few years machine learning techniques have been employed to considerably push the field forward. In this project, expertise in speech enhancement and X-ray tomography are combined to make improvements in both fields.