Are you interested in a scientific career in big data analytics revealing obscured patterns, and uncovering correlations and insights? The Structural Molecular Biology (SMB) Division at SSRL is seeking a passionate and talented Research Associate with a strong background in MX data processing and/or software development programming skills to help develop and release analytical pipelines and frameworks for processing large volumes of crystallographic data at the Stanford Synchrotron Radiation Lightsource (SSRL). The candidate will contribute to a dynamic and exciting environment deploying and integrating scientific algorithms for macromolecular crystallography beamlines in an environment that operates at the frontier of biological science. The application of such algorithms will facilitate the research goals of users at SLAC through structural investigations of important biological targets in limited supply, such as membrane proteins and multi-component macromolecular machines. The successful candidate will join an integrated team of engineers, scientists, software developers and other staff members. with a future goal of making stand-alone data processing application usable off-site and at other facilities.
See https://www-ssrl.slac.stanford.edu/smb/ for more on SSRL and the unique capabilities of our SSRL instrument and SMB beamline facilities.
Note: This is a 2-year termed appointment. Assignment duration is contingent upon project needs, availability of funding and performance. Applicants must provide evidence of either a recently completed PhD degree or confirmation of completion of the PhD degree requirements prior to starting the position. Applicants should also include a cover letter, a statement of research area including brief summary of accomplishments, a curriculum vitae, and a list of publications. Supplying letters of recommendation or names to contact for reference with the application is useful, but not required.
SLAC is a U.S. Department of Energy (DOE) laboratory operated by Stanford University and based in Menlo Park; CA. Due to COVID- 19-related curtailment of on-site activities, the job duties for this position may be required to be performed from home until full site access is restored.
Your specific responsibilities include:
- Installation, testing and troubleshooting of new experimental data processing software for synchrotron radiation end station crystallography with help from the other project team members and the SSRL-SMB engineering support staff.
- Participate in the design, development and deployment of software systems for big-data analytics. Integrate novel algorithms with current data analysis software, expanding to highly-parallelized data analysis.
- Develop real-time and offline data analysis framework optimization of x-ray crystallography data processing.
- Publish and present scientific results.
- Other duties may also be assigned.
To be successful in this position you will bring:
- A PhD degree in physics, chemistry, mathematics, electrical engineering, computer science, or a related scientific computing field with demonstrated coursework or experience including:
- Using algorithmic methods to address data analysis, signal processing, or theoretical
- modeling as evidenced by publications/presentations.
- Programming skills with Python and compiled languages like C/C++ and in-depth
- knowledge in Linux/Unix.
- Ability to adapt schedule and solution complexity to meet fixed deadlines with general guidance.
- Exceptional communication skills, including interfacing with administrative, scientific, mechanical and electronics staff.
- Ability to work well in a fast-paced research and development team.
- Strong conceptual and problem-solving skills as well as the ability to identify and recommend solutions.
- Excellent organizational skills and the ability to synthesize complex technical and scientific information.
In addition, preferred requirements include:
- X-ray physics, macromolecular crystallography or related imaging software development.
- Software collaboration and revision tools such as git.
- Applied math libraries and algorithms, preferably NumPy, Matplotlib and FabIO.
- Advanced machine learning with experience in applying neural networks.
- Large-scale software parallelization techniques, preferably MPI.
SLAC employee competencies:
- Effective Decisions: Uses job knowledge and solid judgment to make quality decisions in a timely manner.
- Self-Development: Pursues a variety of venues and opportunities to continue learning and developing.
- Dependability: Can be counted on to deliver results with a sense of personal responsibility for expected outcomes.
- Initiative: Pursues work and interactions proactively with optimism, positive energy, and motivation to move things forward.
- Adaptability:Flexes as needed when change occurs, maintains an open outlook while adjusting and accommodating changes.
- Communication: Ensures effective information flow to various audiences and creates and delivers clear, appropriate written, spoken, presented messages
- Relationships: Builds relationships to foster trust, collaboration, and a positive climate to achieve.
Physical requirements and Working conditions:
- Consistent with its obligations under the law, the University will provide reasonable
- accommodation to any employee with a disability who requires accommodation to perform the essential functions of his or her job Work standards:
- Interpersonal Skills: Demonstrates the ability to work well with Stanford colleagues and clients and with external organizations.
- Promote Culture of Safety: Demonstrates commitment to personal responsibility and value for environment, safety and security; communicates related concerns; uses and promotes safe behaviors based on training and lessons learned. Meets the applicable roles and responsibilities as described in the ESH Manual, Chapter 1—General Policy and Responsibilities: http://www- group.slac.stanford.edu/esh/eshmanual/pdfs/ESHch01.pdf
- Subject to and expected to comply with all applicable University policies and procedures, including but not limited to the personnel policies and other policies found in the University’s Administrative Guide, http://adminguide.stanford.edu
- Classification Title: Research Associate Experimental
- Job Code: 0127
- Duration: fixed term 24 months
To apply for this job please visit erp-hprdext.erp.slac.stanford.edu.