Job ID 1710 Date posted 04.27.2019
Brookhaven National Laboratory is a multipurpose research institution funded primarily by the U.S. Department of Energy’s Office of Science. Located on the center of Long Island, New York, Brookhaven Lab brings world-class facilities and expertise to the most exciting and important questions in basic and applied science—from the birth of our universe to the sustainable energy technology of tomorrow. We operate cutting-edge large-scale facilities for studies in physics, chemistry, biology, medicine, applied science, and a wide range of advanced technologies. The Laboratory’s almost 3,000 scientists, engineers, and support staff are joined each year by more than 4,000 visiting researchers from around the world. Our award-winning history, including seven Nobel Prizes, stretches back to 1947, and we continue to unravel mysteries from the nanoscale to the cosmic scale, and everything in between. Brookhaven is operated and managed by Brookhaven Science Associates, which was founded by the Research Foundation for the State University of New York on behalf of Stony Brook University, and Battelle, a nonprofit applied science and technology organization.
The Structural Biology Group at National Synchrotron Light Source II (NSLS-II) is seeking a post-doctoral researcher to help develop data analysis methods linking protein x-ray solution scattering, cryo-electron microscopy and structural model building and validation. These methods will allow researchers to quickly extract accurate and physiologically-relevant models of biological samples in solution and then use these models to enhance particle picking in electron micrograms and subsequent model building and validation. We expect that the methods produced will be applicable to a wide variety of biological samples. Developing these methods will be the principal responsibility of the post-doc recruited.
Essential Duties and Responsibilities:
The successful candidate will participate in collaborative research with biologists to elucidate the structure of macromolecular complexes using the data collected at X-ray solution scattering beamlines at NSLS-II, and on the electron microscopes available at the laboratory. The primary responsibilities of the successful applicant for this position are to harmonize data analysis methods and develop the machine learning algorithms to implement the data analysis software. The project will benefit from the expertise available at BNLs Computational Science Initiative.
Required Knowledge, Skills, and Abilities:
- Ph.D. in biophysics, physics, computer science, structural biology or related fields.
- Ability to develop software for data analysis, using Python or C/C++.
- Experience with Linux operating systems and GPU acceleration.
- Able to work in a team environment and self-motivated.
Preferred Knowledge, Skills, and Abilities:
- Experience in data collection at a synchrotron beamline and/or electron microscopes
- Knowledge of the structure of proteins
- Experience with integrative analysis of biological structural data, including solution scattering and cryo EM data
- Experience with deep learning using popular platforms such as PyTorch, TensorFlow etc.
BNL policy requires that research associate appointments be made to individuals who have received their doctorate within the past five years.
At Brookhaven National Laboratory we believe that a comprehensive employee benefits program is an important and meaningful part of the compensation employees receive. Our benefits program includes, but is not limited to:
- Medical Plans
- Dental Plans
- Life Insurance
- 401(k) Plan
- Paid Parental Leave
- Swimming Pool, Weight Room, Tennis Courts, and many other employee perks and benefits
We invite you to consider Brookhaven National Laboratory for employment. To be considered for this position, please apply online at BNL Careers and enter the job title into the Keyword Search.
Brookhaven National Laboratory (BNL) is an equal opportunity employer committed to ensuring that all qualified applicants receive consideration for employment and will not be discriminated against on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, status as a veteran, disability or any other federal, state or local protected class.
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