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.

The Signal Processing Group is hiring a research associate (wissenschaftlicher Mitarbeiter) for the project "Crossmodal Processing of Audio-Vidual Signals". The position offered here below is suitable for both a post doctoral researcher or a for a research associate aiming at obtaining a PhD. The associate will have the opportunity to pursue further academic qualifications, for example a doctoral dissertation or a habilitation. Please find the official job posting here.

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The position in accordance with Section 28 subsection 3 of the Hamburg higher education act (Hamburgisches Hochschulgesetz, HmbHG) commences on the soonest possible date. 

This is a fixed-term contract in accordance with Section 2 of the academic fixed-term labor contract act (Wissenschaftszeitvertragsgesetz, WissZeitVG). The term is fixed for a period of 31 months. The position calls for (per week) 39 hours. 

Responsibilities: 

Duties include academic services in the project named above. Research associates may also pursue independent research and further academic qualifications. 

Specific Duties: 

The candidate will work in the Signal Processing group and will do research on modern methods for speech, audio, and audio-visual processing. The focus of the group is on developing novel methods for processing speech signals with applications in speech communication devices such as assistive listening devices, mobile telephony, and voice-controlled assistants. Typically, the performance of these devices drops drastically when interfering noise sources are present. To mend this undesired behavior, noise reduction is applied. 

The goal of the successful candidate is to improve noise reduction systems by incorporating visual information captured by a camera. For this, modern methods from signal processing and machine learning are to be applied. Besides developing new concepts and implementing new algorithms, the typical tasks of a Research Associate include experiments to test the methods, writing scientific publications, and traveling to conferences and workshops to present the work. We are interested in a highly motivated person who is interested in working with us on cutting edge research in a pleasant working atmosphere. 

Requirements: 

A university degree in a relevant field. An academic degree like a master degree is required, a doctorate is desirable, but not conditional. Examples are Computer Science, Electrical Engineering, Physics, Mathematics, etc.

Good knowledge of signal processing and machine learning is required as well as good programming skills in Python, Matlab and/or C++. Knowledge of speech processing, computer vision, and statistics is helpful. Fluent English, spoken and written, and good communication skills are mandatory. Knowledge of German is helpful; we expect the willingness to learn German for non-native German speakers. 

The University aims to increase the number of women in research and teaching and explicitly encourages women to apply. Equally qualified female applicants will receive preference in ac-cordance with the Hamburg act on gender equality (Hamburgisches Gleichstellungsgesetz, HmbGleiG). 

Qualified disabled candidates or applicants with equivalent status receive preference in the ap-plication process. 

For further information, please contact Prof. Timo Gerkmann (timo.gerkmann@uni-hamburg.de) or consult our website at https://uhh.de/inf-sp. 

Applications should include a cover letter, a tabular curriculum vitae, and copies of degree certificate(s). Please send applications by November 30, 2019 to: sp-office@informatik.uni-hamburg.de in a single PDF document. Please start the subject of your Email with (APPLICATION AHOI DIGI-TAL). Please indicate your earliest possible entrance date. 

Please do not submit original documents as we are not able to return them. Any documents sub-mitted will be destroyed after the application process has concluded. 

* Full-time positions currently comprise 39 hours per week

To download this announcement in PDF format, please follow this link.