Project Info
A Postdoctoral Fellow is required to use AI techniques for the integrated structural modelling of macromolecular complexes. The Jan Kosinski group at EMBL Hamburg and the Fabian Theis group at Helmholtz Münich are looking for candidates. The TransFORM collaboration, which is supported by the esteemed ERC Synergy Grant, includes this post. The consortium’s goal is to map the complex functions of the protein translation machinery in human cells by utilising state-of-the-art cryo-electron tomography, mass spectrometry, and computational modelling of atomic structures.
Responsibilities
- Create computational techniques to simulate the atomic structure of large macromolecular complexes by combining your own novel AI-based algorithms with programmes like AlphaFold, RoseTTAfold, and OpenFold. You can also integrate experimental data from mass spectrometry and in-cell cryo-ET tomography.
- Examine and put into practice strategies that integrate advanced deep learning (such as geometric deep learning and reinforcement learning) with non-linear optimisation techniques and statistical tools like Bayesian inference and Monte Carlo sampling.
- Contribute to open-source codebases such as OpenFold, AlphaPulldown, and Assembline,
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Requirements
- Ph.D. in Computer Science, Computational Structural Biology, or a related field.
- Experience with machine learning techniques and frameworks, including Scikit-learn, TensorFlow, PyTorch, or JAX.
- Proficiency in numerical libraries such as NumPy and SciPy.
- Experience in the dissemination and management of software packages.
Deadline and Application
The last date to apply for this position is 19 August 2024. For more details and applications, visit the official website.