Postdoctoral Fellow: AI-based integrative modeling of macromolecular complexes
vor 5 Monaten
Your role
Despite the emergence of AlphaFold and related AI-based modeling tools, large macromolecular complexes still cannot be consistently modeled with high accuracy. Experimental data can guide the modeling of large complexes; however, methods for modeling based on experimental data are not sufficiently automated, computationally inefficient, and lack general applicability. To address this, you will:
Develop computational methods to model the atomic structure of large macromolecular complexes using tools such as AlphaFold, RoseTTAfold, and OpenFold, alongside your own innovative AI-based algorithms, and integrating experimental data from in-cell cryo-ET tomography and crosslinking mass spectrometry Investigate and implement methods that combine statistical techniques like Bayesian inference and Monte Carlo sampling with non-linear optimization approaches and advanced deep learning (e.g., geometric deep learning and reinforcement learning). Contribute to open-source codebases such as OpenFold, AlphaPulldown, and Assembline Disseminate your code in well-organized, documented, and rigorously tested software packages Use these new methods for research projects within the TransFORM consortium.You will be affiliated with the research group of Jan Kosinski at EMBL Hamburg, but you will spend at least a year as a visiting postdoctoral fellow at the Fabian Theis group at Helmholtz Münich.
Apply nowClosing date: 19 August 2024
Contract duration: 3 years with possibility of extension up to 5 years
Grading: Stipend
Reference number: HH00227
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You have
Ph.D. in Computer Science, Computational Structural Biology, or a related field Proficiency in numerical libraries such as NumPy and SciPy Experience with machine learning techniques and frameworks, including Scikit-learn, TensorFlow, PyTorch, or JAX Experience in the dissemination and management of software packages Strong motivation to work in a highly collaborative and multidisciplinary environment of EMBL, Helmholtz Münich, and the TransFORM consortium
You might also have
Experience in computational structural modeling or deep learning. However, candidates with a strong background in either computer science (and a willingness to learn structural biology) or structural biology (with proven extensive programming expertise) are encouraged to apply. Experience in deep learning techniques that can be applied for modeling or analysis of 3D macromolecular structures, such as geometric deep learning, graph neural networks, transformer and attention-based models, and reinforcement learning.-
Hamburg, Deutschland Embl-Ebi VollzeitYour role Despite the emergence of AlphaFold and related AI-based modeling tools, large macromolecular complexes still cannot be consistently modeled with high accuracy. Experimental data can guide the modeling of large complexes; however, methods for modeling based on experimental data are not sufficiently automated, computationally inefficient, and...
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Hamburg, Hamburg, Deutschland Embl-Ebi VollzeitJob SummaryWe are seeking a highly motivated Postdoctoral Fellow to join our research group at EMBL Hamburg. The successful candidate will work on developing novel computational methods for modeling the atomic structure of large macromolecular complexes using AI-based tools and integrating experimental data.Key ResponsibilitiesDevelop and implement...
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