AI and Machine Learning Scientist
Job ID 2113 Date posted 10/19/2020Brookhaven 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.
Organizational Overview
Brookhaven National Laboratory (BNL) is a scientific, extreme scale Data Laboratory in the US, New York State, Long Island. We support the data and computational needs of on-site facilities such as the Relativistic Heavy Ion Collider (RHIC), the National Synchrotron Light Source II (NSLS II), the Center for Functional Nano Materials and the Atmospheric Radiation Measurement program. In addition, we host the largest data center for the ATLAS experiment at CERN, and provide online and offline data services for the Japanese Belle II experiment. With over 170 PB of archived data BNL hosts one of the top 10 scientific data archives in the world, in 2019 alone we analyzed over 800 PB of scientific data.
As a result, we have a lively, fast-growing data science research program at BNL, with a specific focus on the challenges presented by the analysis, interpretation, and use of data at extreme scales and in real-time. The data science program is accompanied by significant computational modeling research effort, in support of the design, planning, analysis, and interpretation of experiments and their results. The Computational Science Initiative (CSI -https://www.bnl.gov/compsci/) provides a laboratory-wide umbrella for these activities, bringing together computer scientists, applied mathematicians, and domain scientists to carry out leading-edge research, convert research results into practical solutions that advance domain science, and provide the necessary computing infrastructure services and training to support efficient operation.
Position Description
CSI (Computational Science Initiative) is looking for scientist with a passion for using artificial intelligence and machine learning to solve scientific challenges. The candidate will work with domain scientist and design and implement new machine learning algorithms and techniques if the state of the art approaches fail. CSI is looking for ambitious and talented team members to join our AI / ML group. The outcome of research is expected to be published in top tier conferences and journals.
Essential Duties and Responsibilities:
- Research, design and implement novel, highly scalable Deep Learning (DL) algorithms with focus on spatio-temporal data analysis tasks such as climate, high energy physics, etc.
- Strategy development for the field of DL with focus on our lab’s applications, support in strategic decision making with regard to various science problem.
- Promote the research results through scholarly publications and presentations at leading conferences
- Lead or participate in the development of related research proposals.
- Promote collaborative research with interdisciplinary research team.
Position Requirements
Required Knowledge, Skills, and Abilities:
- PhD in Computer Science, Statistics, Applied Math or related discipline
- DL research experience with large data sets in academia or industry.
- Familiarity with deep learning libraries (Theano, Torch, Caffe, TensorFlow, etc.)
- Strong track records of publication in top tier conferences / journals.
- Good oral and written communication and teamwork skills.
Preferred Knowledge, Skills, and Abilities:
- Worked with general ML libraries and toolkits
- Experience with Recurrent Neural Networks and Deep Generative models
- Experience working in multidisciplinary scientific collaboration
- Experience with accelerators (GPU, Intel Xeon Phi) and high performance computing systems
- Track record of producing high quality software on schedule
Other Information:
- The ability to obtain and maintain a DOE “Q” security clearance would be advantageous.
- Moderate domestic and foreign travel
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
- Vacation
- Holidays
- Life Insurance
- 401(k) Plan
- Retirement Plan
Swimming Pool, Weight Room, Tennis Courts, and many other employee perks and benefits
BNL takes affirmative action in support of its policy and to advance in employment individuals who are minorities, women, protected veterans, and individuals with disabilities.We ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment.Please contact us to request accommodation.
*VEVRAA Federal Contractor
Brookhaven employees are subject to restrictions related to participation in Foreign Government Talent Recruitment Programs, as defined and detailed in United States Department of Energy Order 486.1A. You will be asked to disclose any such participation at the time of hire for review by Brookhaven. The full text of the Order may be found at: https://www.directives.doe.gov/directives-documents/400-series/0486.1-BOrder-a/@@images/file
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