Serial Crystallography Research Scientist

at  Lawrence Berkeley National Laboratory

Berkeley Lab’s (LBNL) Molecular Biophysics and Integrated Bioimaging Division (MBIB) has an opening for a Computational Research Scientist to join the Structural Biology Department.
In this exciting role, you will be a part of a new project to bring serial crystallography to the Advanced Light Source (ALS). This role will include creating a modular software pipeline for the analysis of serial X-ray diffraction patterns using the Diffraction Integration of Advanced Light Source (DIALS) library that can be redeployed at end stations throughout ALS or beyond.
You will develop new methods of automated instrument control to allow user-free operation of serial crystallography sample delivery mechanisms, and collaborate with data scientists in our computing area to design new methods of detecting the presence or absence of ligands in X-ray electron density maps. This will include fast distillation of data from a variety of sources, including instrument cameras, sensors, and the diffraction patterns themselves, to provide real-time steering of the instrument. It will also involve development of slower feedback mechanisms that changes samples and moves the instrument to a true self-driving laboratory.
You will have the opportunity to participate in larger projects in the group, including working on DIALS infrastructure as part of the US R24 DIALS National Resource, participating in X-ray free electron laser (XFEL) data processing development and data analysis, and small-molecule crystallography methods development.
This position has an anticipated start date of January 1, 2025.

DESIRED QUALIFICATIONS:

• A Ph.D. in Biophysics, Bioinformatics, Mathematics, Computer Science, Engineering, Physical Sciences, or a related field.
• Demonstrated ability to develop self-driving systems, whether through instrument support or AI/ML experience in general.
• Familiarity with synchrotron operation and software packages for operation.
• Familiarity with general Python scientific data packages such as NumPy, SciPy, and pandas.
• Familiarity with the DIALS and cctbx packages.
• Expertise in protein crystallographic data analysis and small molecule chemical crystallography.
• Well versed in modern coding practices for scientific computing, including algorithm design and evaluation, source code management under a revision control system like git, and incorporation of readable documentation and comments.

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• Develop and test new methods to automate a self-driving serial crystallographic instrument at the ALS.
• Perform code management, optimization, and pipeline support as part of integration into the larger ALS infrastructure.
• Participate in data collection and processing at X-ray free electron sources.
• Maintain a detailed record of work performed.
• Document and manage code developed using standard source control methods.
• Publish in peer-reviewed journals.
• Present research at seminars and conferences.
• Assist Principal Investigator in developing grant proposals.
• Document and maintain existing and new data processing software.