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Organic Chemist for Machine Learning - Postdoctoral Researcher

Entry Level | Full-time
Postdoctoral/Fellowship | livermore, CA | 01/25/2021

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Company Description

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.

Job Description

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.

In this role you will

  • 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.

Qualifications

  • 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.

Qualifications We Desire

  • 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.

Additional Information

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

Security Clearance

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

California Privacy Notice

The California Consumer Privacy Act (CCPA) grants privacy rights to all California residents. The law also entitles job applicants, employees, and non-employee workers to be notified of what personal information LLNL collects and for what purpose. The Employee Privacy Notice can be accessed here.

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