cloudFISH: Engineer position
Organization: CloudFISH consortium / France BioImaging RI
Position Information:
CloudFISH is a consortium grouping the labs of Marcelo Nollmann (CNRS Montpellier), Florian Mueller (Zimmer team, Pasteur Institute) and Thomas Walter (Mines Paristech). Our groups develop imaging-based technologies to visualize DNA and RNA with high spatial resolution, with the aim of understanding transcriptional regulation and genome organization. Our past developments include FISH-quant, a software package for the sub-cellular detection of RNAs from single molecule FISH images (Mueller, 2013), Hi-M, a multiplexed imaging-based method to trace chromatin architecture and transcriptional status in single cells (Cardozo, 2019), and deep learning-based approaches to identify mRNA localization patterns (Dubois, 2019). The aim of cloudFISH will be to build and deploy user-friendly, scalable tools to analyze multiplexed RNA-FISH and DNA-FISH data using deep learning approaches with remote computing servers.
We are looking for highly motivated candidates to fill a 2-yrs France BioImaging-funded engineer position to participate in the development of image analysis software within the cloudFISH project. The candidate will have expertise in Python programming. Experience with web programming, image analysis, deep learning frameworks, deployment of server applications with containers and Kubernetes will be an advantage but are not compulsory. Candidates are expected to express a strong interest in working at the interface between informatics with physics and biology.
Please visit our websites for further details: Chromatin Biophysics Lab |
Imaging and Modelling | Computer Vision for Bioimage Analysis
Interested applicants should apply at https://bit.ly/3jCtTqK. Please include a CV with references, and your Github/Gitlab handle.
Relevant Publications
Mueller, et al., Nat Methods. 10: 277–278, 2013.
Cardozo Gizzi, et al., Mol Cell 74, Issue 1, p1-222, Apr 2019; Nature Protocols 15(3):840-876, 2020; Espinola, Goetz et al., Nat Genetics 53, pages 477–486 (2021)
R. Dubois et al., IEEE conference (ISBI), pp. 1386–1390, 2019.