A postdoc position is available as part of an international HFSP funded collaboration in the group of Rafael Carazo Salas, University of Bristol UK, from 1 September 2019 (https://research-information.bristol.ac.uk/en/persons/rafael-e-carazo-salas(a7638b29-53e4-49ba-82b5-98b21d82f41f).html).

Making personalised stem cell therapeutics a reality will require that we understand how to predictively engineer in vitro replacement cells and tissues robustly and in a tumour-free manner, on a person-by-person basis.

Towards that goal the Carazo Salas group is establishing innovative experimental and computational tools and pipelines combined with human pluripotent stem cell technologies (hESC, hiPSC), to elucidate the quantitative & mechanistic basis of efficiency, specificity & tumourigenic potential in human pluripotent stem cell differentiation and identify ways to improve personalised tissue engineering.

As part of that we have recently established large-scale, multiday, multicolour time-lapse microscopy pipelines allowing us to follow at single-cell level how ‘live’ human stem cells proliferate and differentiate over time, to better understand why some cells become efficiently programmed into intended target cell types and others do not. In practice this means we routinely image thousands of ‘live’ human stem cells in multiple epifluorescence microscopy channels (to monitor multiple live reporters of their proliferation and fate) every 10 minutes through multiple days, which gives rise to millions of single-cell data points in feature space from which we want to derive predictive information about cell fate.

We are looking to hire a highly motivated and talented computational postdoc with prior expertise in quantitative image analysis (particularly in cell segmentation and tracking from multi-channel time-lapse fluorescence microscopy images) and machine learning (particularly novel approaches like CNNs, GANs) to help us extract from those images information enabling us to understand and predict why each cell makes the fate decision it makes.

The selected computational scientist will work on a daily basis in close collaboration with experimentalists, and also as part of a larger collaboration with groups in Switzerland and the USA.

Applicants interested in this post should hold a PhD in Computational Image Processing, Computer Vision, Machine Learning or a related subject, have an excellent track record and extensive experience with computational image analysis and/or machine learning, and be excited to work in an interdisciplinary environment. Experience working with timelapse microscopy imaging of cells or high-throughput/high-content microscopy is a plus.

For enquiries, please contact Rafael E. Carazo Salas at rc16805@bristol.ac.uk and if you’re not that person please spread the word to somebody who might.

Nature careers ad is found here: https://www.nature.com/naturecareers/job/research-associate-computational-postdoc-university-of-bristol-uob-692681.


The Image Analysis Hub is an open access, equal access core facility committed to offering support in image analysis. Our webpage is: https://research.pasteur.fr/en/team/image-analysis-hub/

What we do.

As part of the C2RT, we strive to ensure the continuity between image acquisition and image analysis. To this end we rely on our expertise in imaging and collaborate with other facilities such as the UTechS-PBI and UTechS-UBI when pertinent. All requests involving images are considered.


Our services follow four axes:

1. Offer walk-in support and trainings for questions involving image analysis.

This activity aims at offering to users quick answers to scientific questions involving well-established pipelines, for which a commercial or published tool exists and can be used conveniently. Users can address their question to the facility during open-desk sessions or directly via one-to-one requests. Depending on the effort involved, the solution is derived and proposed onsite, or individual  trainings are scheduled. For general topics, the Hub organises regular courses and workshops, possibly involving external teachers or providers.

For instance, see below for the announcement of our open-desk, organised regularly every two week.


2. Build and deploy custom analysis tools for projects requiring special developments.

Research endeavours to address original questions, for which analysis tools might be lacking or incomplete. The Image Analysis Hub aims at creating or implementing novel tools based on existing algorithms to address these questions, using skills in image analysis and software development. More than just developing the analysis tool, this activity often involves deriving a suitable analysis methodology, for which the facility expertise in microscopy and biophysics is key. Engineers work in close collaboration with users within the framework of a scientific project over medium or long durations. For projects whose effort would extend beyond typical facility usage or involve original research work, the project may be directed to the BioImage Analysis unit after a discussion with all parts.


3. Maintain an infrastructure for autonomous image analysis. Deal with complex tool deployments.

Data volume and modern analysis techniques may call for a computing power not always present in Pasteur labs. Providing open-access workstations unlock barriers to compute-intensive tools. They also act as the central sharing points for commercial softwares, making them available to the whole campus. Finally, some specialized tools require special deployment efforts, e.g. to make such a tool able to exploit the HPC infrastructure of the Institut Pasteur.


4. Develop original and innovative software tools for image analysis, whose scope exceed user projects.

Software development and image analysis skills of the facility can be leveraged to build ambitious software tools shipping innovative technologies. These tools exceed the scope of single projects and address the unarticulated needs of the Pasteur community and their creation is part of the development activity of the facility.

THE ROLE  In this role, you will play a leadership role in supporting the development of new tools and facilitating collaborations in the imaging and microscopy community, especially in the areas of image analysis and visualization for cell biology. Our support of this ecosystem will likely take many forms, including grantmaking to biologists and computational scientists and developing open-source tools among the science community and CZI computational biologists and software engineers. The ideal candidate will have deep, hands-on expertise in quantitative microscopy, and at least some familiarity with the current open-source imaging software ecosystem (e.g. ImageJ, FIJI. CellProfiler, etc.) and core technologies (Python, TensorFlow, WebGL). Key qualities are (1) an understanding of how biologists want to work with imaging data; (2) a passion for community engagement and open-source development; and (3) strong interpersonal skills and ability to build and work across a diversity of expertise.


  • Work closely with Science Program Officers, Computational Biologists, and Technology Staff to help define and lead programs in imaging and microscopy and represent them to the outside community
  • Collaborate with biologists, imaging scientists, and open-source software developers to help conceive and develop analysis and visualization tools for biological imaging data
  • Drive internal and external community engagement, including planning and running hackathons and meetings, and engage in outreach to ensure connections to users, standards groups, and existing open-source communities
  • Support the grantmaking process for current and future programs, including selection, review, process, and relationship management
  • Identify and engage with existing open-source communities, and help build consensus around standards and methods


  • PhD degree or equivalent experience in quantitative microscopy or similar field
  • 3+ years of experience analyzing microscopy image data, including the use of common open-source tools (e.g. ImageJ, FIJI, CellProfiler) and at least one modern software analysis or visualization ecosystem (e.g. Python, TensorFlow, WebGL, etc.)
  • 3+ years of experience in physical or virtual community-building activities, including hackathon planning, workshop or conference organization, hosting tutorials, etc.
  • 3+ years working in or helping coordinate open-source software communities