RESEARCH FIELDPhysics › Computational physics
RESEARCHER PROFILEFirst Stage Researcher (R1)
APPLICATION DEADLINE04/04/2020 00:00 - Europe/Brussels
LOCATIONGermany › Tübingen
TYPE OF CONTRACTTemporary
HOURS PER WEEK40
Using today’s computing power and software packages it has become possible to analyze large and multidimensional experimental scattering data. The process of converting these data into useful scientific information, however, can be challenging. Popular machine learning models, such as artificial neural networks, have recently shown significant advantages in terms of speed over other computational methods that are usually employed to extract the essential parameters of the investigated systems .
Within the field of soft matter physics, our group studies the fundamental structural properties, particularly the growth process, of organic thin films [2,3]. In this context, we collect X-ray scattering data using highly specialized synchrotron beamlines, e.g. at the ESRF in Grenoble. Modern area detector technology allows us to record enormous amounts of complex data, however, usually data analysis remains the bottleneck for the scientific output.
 A. Greco et al., Fast fitting of reflectivity data of growing thin films using neural networks, J. Appl. Cryst. 52 (2019) 1342
 C. Frank et al., Analysis of island shape evolution from diffuse x-ray scattering of organic thin films and implications for growth. Phys. Rev. B 90 (2014) 205401
 J. Dieterle et al., Structural properties of picene-perfluoropentacene and picene-pentacene blends: Superlattice formation versus limited intermixing. J. Phys. Chem. C 119 (2015) 26339
REQUIRED EDUCATION LEVELPhysics: PhD or equivalent
REQUIRED LANGUAGESENGLISH: Good
Candidates with a background in computational methods and programming with an interest in soft-matter physics
EURAXESS offer ID: 475074
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