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.
Internal Number: 3743990000021786
About Lawrence Livermore National Laboratory
For more than 60 years, the Lawrence Livermore National Laboratory has applied science and technology to make the world a safer place.Livermore’s defining responsibility is ensuring the safety, security and reliability of the nation’s nuclear deterrent. Yet LLNL’s mission is broader than stockpile stewardship, as dangers ranging from nuclear proliferation and terrorism to energy shortages and climate change threaten national security and global stability. The Laboratory’s science and engineering are being applied to achieve breakthroughs for counterterrorism and nonproliferation, defense and intelligence, energy and environmental security.
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