Your role
1. Develop algorithms to identify macromolecular complexes in cellular tomograms, using image processing techniques such as template matching and machine learning
2. Investigate and implement methods to enhance the speed and accuracy of particle identification by increasing algorithm efficiency, refining scoring functions, and applying the latest deep learning techniques in computer vision
3. Explore the integration of spatial constraints from other data sources to improve particle identification
4. Co-develop PyTME (https://github.com/KosinskiLab/pyTME)
5. Disseminate your code in well-organized, documented, and rigorously tested software packages
6. Use these new methods for research projects within the TransFORM consortium
Apply now
Closing date: 19 August 2024
7. Contract duration: 3 years (with possibility of extension up to 5 years)
8. Grading: Stipend
9. Reference number: HH00228
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You have
13. Ph.D. in Computer Science, Image Processing, Computational Structural Biology, or a related field
14. Advanced programming skills in Python and a proven track record in the development and application of computational tools
15. Proficiency in numerical libraries such as NumPy and SciPy
16. A solid foundation in mathematics and experience in algorithm development
17. At least basic knowledge of machine learning techniques
18. Experience in the dissemination and management of software packages
19. Strong motivation to work in a highly collaborative and multidisciplinary environment of EMBL and the TransFORM consortium
You might also have
20. Experience in cryo-ET data analysis or machine learning. However, candidates with a strong background in either computer science (and a willingness to learn cryo-ET data analysis) or cryo-ET (with proven extensive programming expertise) are encouraged to apply.
21. Experience with deep learning frameworks such as PyTorch, TensorFlow, or JAX.