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The CENEM, a well-established expertise center and user facility for electron microscopy and nanoanalysis at FAU, runs a number of state-of-the-art microscopes in the field of transmission electron microscopy, scanning electron microscopy and X-ray microscopy, as well as a well-equipped laboratory for sample preparation. The IMN, a research institute of the Materials Science Department, explores the advanced capabilities of CENEM’s instrumentation for cutting edge research on new materials, a major research focus of the university.
The position is embedded in the Collaborative Research Center (CRC) 1411, which is an initiative funded since 2020 by DFG focused on predictive model based design and practical process realisation of particulate products. These range from single nanoparticles with controlled shape and composition, through hierarchically organised assemblies of particles, up to particle packings with optimised optical properties. Such particles are produced by continuous syntheses directly coupled to classification by chromatography. The research is carried out at FAU Erlangen-Nürnberg and involves the Departments of Chemical and Biological Engineering, Materials Science and Engineering, Mathematics, Chemistry, and Physics. One project is located at the NanoEnergieTechnikZentrum of the University of Duisburg-Essen.
Benefits: We Have a Lot To Offer
Regular promotion to the next level and increase in salary pursuant to the collective bargaining agreement for the public service of the German Länder (TV-L) or remuneration pursuant to the Bavarian Public Servants Remuneration Act (BayBesG) plus an additional annual bonus
30 days annual leave at five working days per week with additional free days on December 24 and 31
Occupational pension scheme and asset accumulation savings scheme

POSITION OVERVIEW:

We invite applications for a motivated doctoral researcher to join our interdisciplinary team specializing in scale-bridging correlative tomography to investigate hierarchical porous structures. The successful candidate will focus on characterizing stationary phase materials (SPMs) used in chromatography, as well as other porous materials, utilizing state-of-the-art imaging techniques such as electron tomography (ET), nano X-ray computed tomography (nano-CT), and micro-computed tomography (micro-CT).

YOUR QUALIFICATIONS:

Master’s degree or equivalent in Materials Science, Physics, Chemical Engineering, or a closely related field with competitive grades.
Strong interest in tomographic imaging techniques and data analysis.
Good communication skills and the ability to work both independently and collaboratively.
Able to work in an interdisciplinary team, high level of independence and diligence.
Able to present ideas and results to a broad audience.
Proficient in the English language; proficiency in the German language is beneficial though not essential.

DESIRABLE QUALIFICATIONS:

Prior experience with SEM/FIB, TEM or X-ray microscopy methods is a plus but not required.
Experience with tomographic imaging techniques and data analysis.
Programming skills and familiarity with image processing and analysis software.
A keen interest in advancing methodological developments in tomography and materials characterization.
Additional Information
FAU is a member of “The Family in Higher Education Institutions” best practice club and aims to increase the number of women in scientific positions. Female candidates are therefore particularly encouraged to apply.

Conduct detailed 3D characterization of porous and particulate materials on different scales with the overarching goal of understanding irregularities and designing optimized porous structures.
Develop tailored sample preparation workflows, including laser ablation and focused ion beam (FIB), for correlative imaging experiments.
Refine workflows and algorithms for data acquisition, reconstruction, and registration to enhance correlative 3D reconstructions.
Implement advanced machine and deep learning segmentation strategies to improve quantitative analysis of hierarchical pore networks.
Collaborate with material synthesis teams to optimize SPM structures based on imaging feedback.
Analyze 3D pore data to support simulations of diffusion, transport, and particle self-assembly.
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