Research Scientist: AI-Driven Modeling of Macromolecular Structures
vor 4 Wochen
Your Role
In the realm of structural biology, accurately modeling large macromolecular assemblies remains a significant challenge, even with advancements from AI technologies like AlphaFold. While experimental data holds potential for enhancing these models, existing methodologies are often not automated, computationally intensive, and lack widespread applicability. In this position, you will:
- Develop innovative computational strategies to accurately depict the atomic architecture of extensive macromolecular complexes, utilizing tools such as AlphaFold, RoseTTAfold, and OpenFold, while integrating your own AI-driven algorithms and experimental data from in-cell cryo-ET tomography and crosslinking mass spectrometry.
- Explore and apply techniques that merge statistical methods, including Bayesian inference and Monte Carlo sampling, with advanced optimization and deep learning approaches (e.g., geometric deep learning and reinforcement learning).
- Contribute to open-source projects such as OpenFold, AlphaPulldown, and Assembline.
- Ensure your software is well-organized, thoroughly documented, and rigorously tested before dissemination.
- Utilize these novel methodologies for research initiatives within the TransFORM consortium.
You will be part of the research team led by Jan Kosinski at EMBL Hamburg, with a commitment to spend a significant portion of your tenure collaborating with the Fabian Theis group at Helmholtz Münich.
Contract Details:
- Duration: 3 years, with the possibility of extension up to 5 years.
- Compensation: Stipend.
- Reference Number: HH00227.
Qualifications:
- Ph.D. in Computer Science, Computational Structural Biology, or a related discipline.
- Strong proficiency in numerical libraries such as NumPy and SciPy.
- Experience with machine learning frameworks, including Scikit-learn, TensorFlow, PyTorch, or JAX.
- Demonstrated experience in the management and dissemination of software packages.
- A keen interest in working within a collaborative and multidisciplinary environment involving EMBL, Helmholtz Münich, and the TransFORM consortium.
Preferred Qualifications:
- Background in computational structural modeling or deep learning is advantageous. Candidates with a solid foundation in either computer science (willing to learn structural biology) or structural biology (with strong programming skills) are encouraged to apply.
- Familiarity with deep learning techniques applicable to the modeling or analysis of 3D macromolecular structures, such as geometric deep learning, graph neural networks, and transformer-based models.
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