JBEI Postdoctoral Associate - Machine Learning

Posting Duration:

This posting will be open for application submissions for a minimum of seven (7) calendar days, including the ‘posting date’. Sandia reserves the right to extend the posting date at any time.

NNSA Requirements for MedPEDs:

If you have a Medical Portable Electronic Device (MedPED), such as a pacemaker, defibrillator, drug-releasing pump, hearing aids, or diagnostic equipment and other equipment for measuring, monitoring, and recording body functions such as heartbeat and brain waves, if employed by Sandia National Laboratories you may be required to comply with NNSA security requirements for MedPEDs.

If you have a MedPED and you are selected for an on-site interview at Sandia National Laboratories, there may be additional steps necessary to ensure compliance with NNSA security requirements prior to the interview date.

What Your Job Will Be Like:

Are you looking for an exciting opportunity to do creative and important work that makes a difference? Are you excited to perform innovative research in cheminformatics, bioinformatics, data and predictive science? If you are passionate about increasing your engagement with research that supports development of biofuels and bio-products, join this dedicated and hardworking team as a Postdoctoral Appointee!

We are seeking a highly innovative candidate to join the Predictive Deconstruction team at the Joint BioEnergy Institute (JBEI, https://www.jbei.org/), a U.S. Department of Energy (http://science.energy.gov/) (DOE) Bioenergy Research Center dedicated to developing advanced biofuels/liquid fuels derived from the solar energy stored in plant biomass that can replace gasoline, diesel and jet fuels. The selected candidate will collaborate with the group to develop machine learning algorithms for predicting properties of ionic liquids and deep eutectic solvents that are associated with efficient biomass solubilization, pretreatment and deconstruction into simple sugars and aromatics that are amenable to uptake and conversion by to valuable biofuels and biochemicals by engineered host organism. You will need proficiency in cheminformatics, data analytics and machine learning, particularly in encoding small molecules for use in machine learning algorithms. As a member of the team, you will work on multi-disciplinary projects with a wide range of experts to conduct innovative R&D in lignocellulosic biofuels.

On any given day, you may be called on to:

• Develop and implement cheminformatics and machine learning models to predict properties of ionic liquids and deep eutectic solvents
• Develop and implement cheminformatics and machine learning models to predict the deconstruction efficacy of ionic liquids and deep eutectic solvents
• Work with physics-based molecular modeling experts to develop strategies for extracting and engineering features
• Help in designing solvents capable of efficiently fractionating and deconstructing lignocellulosic biomass
• Participate in laboratory management and mentoring student interns
• Enhance your professional development by presenting results at project meetings and conferences
• Publish scientific papers in peer-reviewed journals
• Implement, monitor, and refine project plans and proposals

Qualifications We Require:

• PhD in cheminformatics, mathematics, computer science, machine learning, artificial intelligence, or a closely related field with an emphasis on building, evaluating, and applying predictive models
• Informatics skills and familiarity with machine learning software such as Scikit-Learn, Keras, TensorFlow or similar tools for developing predictive models
• The ability to conduct and communicate creative, independent research as identified through a record of high technical accomplishments (e.g., publication in high-impact scientific journals, presentations at national/international conferences, etc.)

Qualifications We Desire:

• Knowledge of ionic liquids and deep eutectic solvents applied to biomass pretreatment
• Experience with featurization of small molecules for use in machine learning and artificial intelligence algorithms
• Experience in statistical experimental design, particularly in designing experiments to fill in missing data to improve machine learning models
• Basic understanding of molecular modeling using quantum mechanical and molecular mechanical simulations

About Our Team:

Sandia’s Computational Biology and Biophysics Department supports biodefense, emerging infectious disease and biofuels mission areas. The department has core capabilities in bioinformatics, machine learning, bioimaging, molecular modeling and the cybersecurity of biological operations. The focus of the department is in developing innovative research, using advance computational methods, to solve national security problems in biology. We conduct research in advanced RAMAN spectroscopy, hyperspectral imaging, enzymatic lignin valorization, biomimetic membrane design, the design of synthetic microbial consortia for biological breakdown and conversion of biological materials to valuable chemicals and fuels, and machine learning for the development of algal pond consortia. This research is supported by the National Institutes of Health, Bioenergy Technologies Office, Biological and Environmental Research, Basic Energy Sciences, the Department of Defense, Defense Threat Reduction Agency, DOE Energy I-Corps, the Center for Integrated Nanotechnologies, Academia, Industry, Department of Health and Human Services and Sandia’s Laboratory Directed Research and Development program.

About Sandia:

Sandia National Laboratories is the nation’s premier science and engineering lab for national security and technology innovation, with teams of specialists focused on cutting-edge work in a broad array of areas. Some of the main reasons we love our jobs:

• Challenging work with amazing impact that contributes to security, peace, and freedom worldwide
• Extraordinary co-workers
• Some of the best tools, equipment, and research facilities in the world
• Career advancement and enrichment opportunities
• Flexible work arrangements for many positions include 9/80 (work 80 hours every two weeks, with every other Friday off) and 4/10 (work 4 ten-hour days each week) compressed workweeks, part-time work, and telecommuting (a mix of onsite work and working from home)
• Generous vacations, strong medical and other benefits, competitive 401k, learning opportunities, relocation assistance and amenities aimed at creating a solid work/life balance*

World-changing technologies. Life-changing careers. Learn more about Sandia at: http://www.sandia.gov*These benefits vary by job classification.

Security Clearance:

This position does not currently require a Department of Energy (DOE) security clearance.

Sandia will conduct a pre-employment drug test and background review that includes checks of personal references, credit, law enforcement records, and employment/education verifications. Furthermore, employees in New Mexico need to pass a U.S. Air Force background screen for access to Kirtland Air Force Base. Substance abuse or illegal drug use, falsification of information, criminal activity, serious misconduct or other indicators of untrustworthiness can cause access to be denied or terminated, resulting in the inability to perform the duties assigned and subsequent termination of employment.

If hired without a clearance and it subsequently becomes necessary to obtain and maintain one for the position, or you bid on positions that require a clearance, a pre-processing background review may be conducted prior to a required federal background investigation. Applicants for a DOE security clearance need to be U.S. citizens. If you hold more than one citizenship (i.e., of the U.S. and another country), your ability to obtain a security clearance may be impacted.

Members of the workforce (MOWs) hired at Sandia who require uncleared access for greater than 179 days during their employment, are required to go through the Uncleared Personal Identity Verification (UPIV) process. Access includes physical and/or cyber (logical) access, as well as remote access to any NNSA information technology (IT) systems. UPIV requirements are not applicable to individuals who require a DOE personnel security clearance for the performance of their SNL employment or to foreign nationals. The UPIV process will include the completion of a USAccess Enrollment, SF-85 (Questionnaire for Non-Sensitive Positions) and OF-306 (Declaration of for Federal Employment). An unfavorable UPIV determination will result in immediate retrieval of the SNL issued badge, removal of cyber (logical) access and/or removal from SNL subcontract. All MOWs may appeal the unfavorable UPIV determination to DOE/NNSA immediately. If the appeal is unsuccessful, the MOW may try to go through the UPIV process one year after the decision date.

EEO:

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or veteran status and any other protected class under state or federal law.

Job ID: 684709