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Materials Informatics and Machine Learning - Research Scientist
Physical Life Sciences | livermore, CA | 01/18/2021
Job Code: SES.2 Science & Engineering MTS 2 / SES.3 Science & Engineering MTS 3
Organization: Physical and Life Sciences
Position Type: Flexible Term
Security Clearance: Anticipated DOE Q clearance (requires U.S. citizenship and a federal background investigation)
Drug Test: Required for external applicant(s) selected for this position (includes testing for use of marijuana)
Medical Exam: Not applicable
Join us and make YOUR mark on the World!
Are you interested in joining some of the brightest talent in the world to strengthen the United States’ security? Come join Lawrence Livermore National Laboratory (LLNL) where our employees apply their expertise to create solutions for BIG ideas that make our world a better place.
We are looking for individuals that demonstrate an understanding of working in partnership with team peers, who engage, advocate, and contribute to building an inclusive culture, and provide expertise to solve challenging problems.
We have an opening for a Materials Informatics and Machine Learning researcher to conduct a full range of moderate to complex research in the areas of accelerated materials discovery, optimization, development and certification using machine learning and data analysis tools. You will actively participate with and be an integral member of an interdisciplinary team responsible for conducting and supporting research in application of machine learning, data and statistical analysis to chemistry and materials science. This position is in the Functional Material Synthesis & Integration group in the Materials Science Division.
This position will be filled at the SES.2 or SES.3 level based on knowledge and related experience as assessed by the hiring team. Additional job responsibilities (outlined below) will be assigned if hired at the higher level.
In this role you will
- Conduct moderately complex to complex research in application of Machine Learning and data analysis to materials science and chemistry domains to enable development and optimization of materials.
- Contribute to the development of a focused research program aimed at leveraging advances in machine learning and big data tools for chemistry and materials science.
- Independently develop moderately complex to complex methods for analyzing multimodal chemistry and materials science data using machine learning to predict future performance.
- Participate in interactions with inter-organizational contacts and/or external customers.
- Assist in representing the organization by providing input on technical issues for specific projects including preparing and presenting technical reports.
- Prepare written analyses, verbal briefings, and other products that capture and communicate research results.
- Contribute to and actively participate in the development of novel concepts applying machine learning to chemistry and materials science to meet sponsor needs in appropriate national security areas.
- Perform other duties as assigned.
Additional job responsibilities, at the SES.3 level
- Conduct advanced research in application of Machine Learning and data science to materials science domains relevant to Laboratory programs.
- Develop research activities relevant to the needs of Laboratory programs and/or external funding agencies.
- Lead a multidisciplinary team and represent the organization to internal and external sponsors.
- Bachelor’s degree in Materials Science, Chemistry, Computer Science, Computational Engineering, Applied Statistics, Applied Mathematics or a related field, or the equivalent combination of education and related experience.
- Broad experience and fundamental knowledge of developing and applying algorithms in one or more of the following Machine Learning areas/tasks: deep learning, unsupervised feature learning, zero- or few-shot learning, active learning, reinforcement learning, multimodal learning, natural language processing, ensemble methods, scalable online estimation, and probabilistic graphical models.
- Experience in the broad application of one or more higher-level programming languages such as Python, Java/Scala, Matlab, R or C/C++.
- Experience with one or more deep learning libraries such as TensorFlow, PyTorch, Keras, Caffe or Theano.
- Ability to work independently on defined research projects.
- Experience conducting directed research with limited direction.
- Proficient verbal and written communication skills necessary to document research results, and to prepare and present proposals, papers, and reports to internal and external audiences.
- Proficient interpersonal skills and ability to work effectively as part of a multi-disciplinary team environment.
Additional qualifications at the SES.3 level
- PhD in Materials Science, Chemistry, Computer Science, Computational Engineering, Applied Statistics, Applied Mathematics or the equivalent combination of education and related experience.
- Significant experience with Machine Learning and data analysis tools.
- ·Advanced written and verbal communication skills necessary to deliver presentations and prepare written reports, explain technical information, and provide advice to management.
Qualifications We Desire
- Comprehensive knowledge and experience with Machine Learning algorithm development and with deep learning model development using TensorFlow, PyTorch, Keras, Caffe or Theano.
Why Lawrence Livermore National Laboratory?
- Included in 2020 Best Places to Work by Glassdoor!
- Work for a premier innovative national Laboratory
- Comprehensive Benefits Package
- Flexible schedules (*depending on project needs)
- Collaborative, creative, inclusive, and fun team environment
Learn more about our company, selection process, position types and security clearances by visiting our Career site.
LLNL is a Department of Energy (DOE) and National Nuclear Security Administration (NNSA) Laboratory. Most positions will require a DOE L or Q clearance (please reference Security Clearance requirement). If you are selected, we will initiate a Federal background investigation to determine if you meet eligibility requirements for access to classified information or matter. In addition, all L or Q cleared employees are subject to random drug testing. An L or Q clearance requires U.S. citizenship. If you hold multiple citizenships (U.S. and another country), you may be required to renounce your non-U.S. citizenship before a DOE L or Q clearance will be processed/granted. For additional information please see DOE Order 472.2.
Equal Employment Opportunity
LLNL is an affirmative action and equal opportunity employer that values and hires a diverse workforce. All qualified applicants will receive consideration for employment without regard to race, color, religion, marital status, national origin, ancestry, sex, sexual orientation, gender identity, disability, medical condition, pregnancy, protected veteran status, age, citizenship, or any other characteristic protected by applicable laws.
If you need assistance and/or a reasonable accommodation during the application or the recruiting process, please submit a request via our online form.
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