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Come join Lawrence Livermore National Laboratory (LLNL) where we apply science and technology to make the world a safer place; now one of 2020 Best Places to Work by Glassdoor!
We have an opening for a Postdoctoral Researcher to conduct research (using both experimental wet lab approaches and machine learning methods) in the development, design, and discovery synthesis of organic small molecule materials. You will be part of an interdisciplinary team of materials scientists, chemists, and computer scientists working to interface organic chemistry with machine learning techniques to accelerate the design and development of organic small molecules with targeted properties. This position is in the Functional Materials Synthesis and Integration Group of the Materials Science Division.
- Design, implement, analyze, and resolve synthetic problems and establish solutions leading to the preparation and purification of organic materials that meet the desired specifications. This work includes use of a Schlenk line, glovebox, LC/LCMS, GCMS, XRD, DSC, NMR, FTIR, Raman, amongst other common analysis tools and techniques in organic chemistry synthesis.
- Evaluate state-of-the-art computational tools that aide in synthesis strategies.
- Elucidate via a combination of wet lab and machine learning statistical techniques the underlying chemistry-function relationships to guide improvements in molecular design and synthesis.
- Contribute to and actively participate in the conception, design and execution of research to address defined problems.
- Pursue independent but complementary research interests and interact with a broad spectrum of scientists internally and externally to the Laboratory.
- Collaborate with scientists in a multidisciplinary team environment to accomplish research goals.
- Maintain and establish laboratory protocols.
- Document research; publish papers in peer-reviewed journals, and present results within the DOE community and at conferences.
- Perform other duties as assigned.
- PhD in chemistry, materials science, chemical engineering or related field.
- Broad experience with organic small molecule synthesis and wet lab chemistry techniques.
- Experience in one or more higher-level programming languages such as Python, Java/Scala, Matlab, R or C/C++.
- Experience with data science techniques, including supervised and/or unsupervised learning approaches.
- Ability and willingness to travel.
- Ability to develop independent research projects and publish in peer-reviewed literature.
- Proficient verbal and written communication skills as reflected in effective presentations at seminars, meetings and/or teaching lectures.
- Initiative and interpersonal skills with desire and ability to work in a collaborative, multidisciplinary team environment.
- Experience with one or more deep learning libraries such as TensorFlow, PyTorch, Keras, Caffe or Theano.
- Experience with energetic materials, including handling and synthesis.
- Experience 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.
Pre-Employment Drug Test: External applicant(s) selected for this position will be required to pass a post-offer, pre-employment drug test. This includes testing for use of marijuana as Federal Law applies to us as a Federal Contractor.
Security Clearance: This position requires a Department of Energy (DOE) Q-level clearance.
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. Q-level 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.
Note: This is a two year Postdoctoral appointment with the possibility of extension to a maximum of three years. Eligible candidates are those who have been awarded a PhD at time of hire date.
Lawrence Livermore National Laboratory (LLNL), located in the San Francisco Bay Area (East Bay), is a premier applied science laboratory that is part of the National Nuclear Security Administration (NNSA) within the Department of Energy (DOE). LLNL's mission is strengthening national security by developing and applying cutting-edge science, technology, and engineering that respond with vision, quality, integrity, and technical excellence to scientific issues of national importance. The Laboratory has a current annual budget of about $2.3 billion, employing approximately 6,900 employees.
LLNL is an affirmative action/ equal opportunity employer. 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, protected veteran status, age, citizenship, or any other characteristic protected by law.