We are looking for a postdoctoral fellow at the SMIS (Synchrotron Microscopy and Infrared Spectroscopy) beamline of the SOLEIL Synchrotron to join the UltraSNOM project, "Ultrasensing in the nearfield: polariton enhanced molecular fingerprinting", an ANR/DFG funded French/German binational research project.
In the framework of UltraSNOM we will develop and investigate a new nanophotonic chemical analysis modality to reach ultra-high detection sensitivity. We will integrate scattering-type Scanning Near-field Optical Microscopy (sSNOM) with graphene devices and couple the light in the near-field into the devices, also containing a minute amount of analyte. Spatial resolution will reach up to twenty nanometre lateral resolution, which translates to extremely small amounts of probed volumes. Through coupling between the plasmon polaritons of graphene and the analyte's molecular vibrations, we will be able to achieve ultrahigh detection sensitivity. Experiments will spread from the terahertz to the far-infrared, which are relevant in chemical fingerprinting. To achieve the best possible coupling and detection, we will model, characterize and optimize the AFM tips and employ thermoelectric detection in the terahertz where other types of detectors are missing. Compressed sensing will be used to enhance the signal-to-noise ratio of the measurements as less time will be necessary to accumulate a single spectrum.
The postdoc will work at the SMIS beamline under the supervision of Ferenc Borondics. They will participate in the UltraSNOM research project and will be granted in-house beam time while also submitting requests to the peer review committees. They will be encouraged to collaborate with beamline staff and users and may also pursue their own research interests. Publishing scientific results and presenting them at national and international conferences will be essential.
The postdoc will work on several topics in the framework of UltraSNOM:
We’re looking for a motivated, curious, dynamic, autonomous person who strives working in a team.
The candidate should hold a PhD in a relevant scientific discipline (chemistry, physics, near-field optics, materials science, etc.) and have experience with near field experiments and data analysis. Experience with scientific data modeling and analysis at the coding level, preferably with Python is considered to be a strong advantage.
An excellent command of the English language and proven track record with publishing scientific papers is essential.