Work Schedule
Standard (Mon-Fri)
Environmental Conditions
Office
Job Description
We are a part of Thermo Fisher Scientific's Advanced Technology research group in the Materials and Structural Analysis Division. Our focus is on Digital Science research, developing new algorithms and applying artificial intelligence (AI) to address complex challenges in electron microscopy. Our team operates across multiple locations, including Bordeaux, France; Eindhoven, The Netherlands; Brno, Czech Republic; and Portland, OR, USA.
Discover Impactful Work:
Imaging systems, like electron microscopes, have advanced in data acquisition, collecting more data at a faster rate. This has driven the success of deep learning (DL) in image processing. However, in the field of cryogenic electron tomography (cryo-ET) only scarce annotated data are available being a bottleneck for demonstrating the usage of deep learning in this field. Indeed, tomograms have a low signal noise ratio, with strong missing wedge artefact, navigating in 3D on such tomograms is laborious and time consuming. An alternative is to use simulation to generate a digital twin of the acquired cellular environment. In this internship we propose to evaluate the benefit of using a digital twin model to train DL models for the processing of cryogenic electron tomograms.
Keys to Success:
This internship, based in Bordeaux, France, offers the chance to support our research team in developing proof of concept projects. You will collaborate closely, conduct research, analyze data, and contribute to innovative solution design and execution. This hands-on experience provides valuable insights into research and development, working with experienced engineers to bring innovative concepts to life.
Education
The ideal candidate should be in the final stages of their engineering master's degree, with a strong grasp of computer science, signal processing, applied mathematics and deep learning.
Knowledge, Skills, Abilities
- Proficient in efficient Python/PyTorch programming
- Understanding of fundamental image processing techniques.
- Solid foundation in mathematics and algorithms for image processing.
- Excellent problem-solving and critical thinking skills.
- Effective communication of complex technical concepts to collaborators.
