The successful candidate will be part of the SLN research lab led by Prof. Dr. Pablo Loza-Alvarez. Tentative starting date on 01/12/2023 (negotiable).
This project will bring screening with Imaging Flow Cytometry (IFC) to the next level, opening the way to clinical diagnostics, making it accessible to a large range of users, democratizing its adoption. The project will connect closer the fields of microfluidics, microfabrication, microscopy and machine learning, complementing the training of the researcher with expertise with in-vitro diagnostics. The researcher will fully exploit the capabilities of the existing IFC instruments and initiate the development of the next generation automatic and data-driven diagnosis.
Thus, in the framework of imaging flow cytometry and in the identification and characterizing of tumor biomarkers in blood derived samples, the candidate will be trained by addressing the following scientific challenges throughout the actions below:
- develop optical, cell manipulation and fluidics lab on chips (mm-scaled);
- extend the capabilities of imaging flow cytometry with novel contrast mechanisms;
- high resolution imaging at, and beyond, the diffraction limit (<100 nm);
- three-dimensional label-free acquisition at high rate (>100 volumes/s).
- Preparing biological samples for optimal imaging.
- Developing and implementing new and advanced novel imaging instruments, based on Light Sheet Fluorescence Microscopy (LSFM).
- Analyzing complex sets of big-data images to extract relevant information using Deep learning strategies.
- Using processed images to design computational and mathematical
Secondment and international collaborations: The project involves an exciting network of collaborators and will include several secondments abroad in top European institutes and companies.
Requirements and conditions
Candidates must hold a degree in physics, physics engineer, and an internationally-recognized master degree (or evidence of its completion in the nearest future) in physics, bio-engineering, electrical engineering or a related field.
Good knowledge in:
- Implementation of optical setups
- Deep learning techniques (Numpy, Pandas, Seaborn, etc.
- Control software and programming skills (LabView, Arduino, etc.)
- Image processing (skimage, Python, scipy, etc)
It will be evaluated favorably if the candidate has experience in spectroscopy and microscopy techniques, and in handling biological samples. Likewise, it will be desirable for the candidate to demonstrate good communication skills and command of the English language.
The candidate should hold appropriate expertise from her/his studies or MSc project to contribute to the research field described above.
The formal application should be submitted online via our currently open PhD Call: https://jobs.icfo.eu/?detail=806
Candidates may contact firstname.lastname@example.org for informal enquiries regarding the application, as well as address scientific enquiries to Prof. Dr. Pablo Loza-Alvarez (email@example.com). Deadline: September 26, 2023.