Materials Science - Machine Learning - Postdoctoral Researcher

We have an opening for a  Postdoctoral Researcher - Machine Learning to conduct research in cheminformatics and materials informatics. You will be part of an interdisciplinary team of computer scientists, materials scientists, and chemists applying existing and developing new machine learning techniques to accelerate the design and development of materials and organic molecules. This position is in in the Functional Materials Synthesis and Integration Group of the Materials Science Division.

This position offers a hybrid schedule, blending in-person and virtual presence. You will have the flexibility to work from home 30% time. 

You will

• Develop cutting-edge machine learning and data science methods to aide in the discovery of new molecular and polymeric compounds with targeted properties.  
• Utilize machine learning and data science techniques to automate the extraction of chemical data, knowledge, and underlying chemistry-function relationships to guide improvements in material design.
• Adapt and improve upon existing traditional machine learning methods to be amenable to challenges in the domain of chemistry and materials science.
• Contribute to and actively participate in the development of novel concepts applying machine learning to chemistry and materials science to solve critical materials science challenges.
• Document research; publish papers in peer-reviewed journals, and present results within the DOE community and at conferences.
• 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.
• Perform other duties as assigned.

• Ability to secure and maintain a U.S. DOE Q-level security clearance which requires U.S. Citizenship.
• PhD in Chemical Engineering, Chemistry, Materials Science, Mathematics or related field.
• Experience in one or more higher-level programming languages such as Python, Java/Scala, Matlab, R or C/C++.
• 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, natural language processing, multimodal learning, ensemble methods, scalable online estimation, and probabilistic graphical models.
• Experience with one or more deep learning libraries such as TensorFlow, PyTorch, scikit-learn, Keras, Caffe or Theano.
• 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

• Knowledge of or experience with applying machine learning to scientific domains.
• Experience working with variety of types of input data for machine learning (images, molecules, text).

#LI-Hybrid

Position Information

This is a Postdoctoral appointment with the possibility of extension to a maximum of three years, open to those who have been awarded a PhD at time of hire date.

Why Lawrence Livermore National Laboratory?

• Included in 2024 Best Places to Work by Glassdoor!
• Flexible Benefits Package
• 401(k)
• Relocation Assistance
• Education Reimbursement Program
• Flexible schedules (*depending on project needs)
• Inclusion, Diversity, Equity and Accountability (IDEA) - visit https://www.llnl.gov/diversity
• Our core beliefs - visit https://www.llnl.gov/diversity/our-values 
• Employee engagement - visit https://www.llnl.gov/diversity/employee-engagement

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. Also, all L or Q cleared employees are subject to random drug testing.  Q-level clearance requires U.S. citizenship. 

Pre-Employment Drug Test

External applicant(s) selected for this position must 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.

Wireless and Medical Devices

Per the Department of Energy (DOE), Lawrence Livermore National Laboratory must meet certain restrictions with the use and/or possession of mobile devices in Limited Areas. Depending on your job duties, you may be required to work in a Limited Area where you are not permitted to have a personal and/or laboratory mobile device in your possession.  This includes, but not limited to cell phones, tablets, fitness devices, wireless headphones, and other Bluetooth/wireless enabled devices.  

If you use a medical device, which pairs with a mobile device, you must still follow the rules concerning the mobile device in individual sections within Limited Areas.  Sensitive Compartmented Information Facilities require separate approval. Hearing aids without wireless capabilities or wireless that has been disabled are allowed in Limited Areas, Secure Space and Transit/Buffer Space within buildings.

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We are an equal opportunity employer that is committed to providing all with a work environment free of discrimination and harassment. 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.

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Our goal is to create an accessible and inclusive experience for all candidates applying and interviewing at the Laboratory.  If you need a reasonable accommodation during the application or the recruiting process, please use our online form to submit a request. 

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