Postdoctoral ML Scientist - Climate-Smart Soil Analytics

We are committed to a diverse and equitable workforce with an inclusive culture that values and celebrates the diversity of our people, talents, ideas, experiences, and perspectives. This is important for continued success of the Laboratory’s mission. Pay Range $ 126,720 Annually Please note that the pay range information is a general guideline only. Many factors are taken into consideration when setting starting pay including education, experience, the external labor market, and internal equity. We have an opening for a Postdoctoral Research Staff Member to contribute to fundamental R&D in machine learning and statistical methods in support of a new project on “Terraforming Soil”. This interdisciplinary project aims to develop agricultural solutions for climate change mitigation. In this role you will develop new multi-fidelity representation learning models to analyze large collections of soil spectra, infer important chemical markers, and account for both epistemic and aleatoric uncertainties in the process. This position will be in the Machine Intelligence Group in the Center for Applied Scientific Computing (CASC) Division within the LLNL Computing Directorate, in collaboration with the Physical and Life Sciences Directorate. You will Research, design, implement, and apply advanced machine learning methods for multiple applications in a collaborative scientific environment. Actively participate with project scientists and engineers in defining, planning, and formulating experimental, modeling, and simulation efforts for complex problems stemming from national security applications. Propose and implement advanced analysis methodologies, collect and analyze data, and document results in technical reports and peer-reviewed publications. Contribute to grant proposals and collaborate with others in a multidisciplinary team environment, including academic and industrial partners, to accomplish research goals. Pursue independent (but complementary) research interests and interact with a broad spectrum of scientists internal and external to the Laboratory. Perform other duties as assigned. Qualifications Recent Ph.D. in Machine Learning, Statistics, Computer Science, Mathematics or a related field. Demonstrated ability and desire to obtain substantial domain knowledge in fields of application in order to communicate effectively with subject matter experts, and to identify novel, impactful applications of machine learning. Experience developing, implementing and applying advanced statistical or machine learning models and algorithms using modern software libraries such as PyTorch, TensorFlow, or similar as evidence through medium to large scale deep learning models and experiments. Demonstrated research productivity, as documented by publications, reports, presentations, and/or open-source software in relevant venues (NeurIPS, ICML, ICLR, CVPR, AAAI, AISTATS, UAI, KDD, JMLR, Nature etc.) Experience with scientific programming in the Python ecosystem as evidence through software artifacts, such as deep learning models, workflows, simulations, or similar Experience with one or more of the following areas of deep learning: representation learning, uncertainty quantification, physics-constrained ML, Bayesian optimization, reinforcement learning, multimodal models, generative models. Qualifications We Desire Experience with high-performance computing, GPU programming, parallel programming, cloud computing, and/or related methods including running numerical simulations of complex workflow Demonstrated technical leadership in fields related to machine learning, such as mentorship or managing teams. Experience or interest in environmental science, climate change mitigation, soil chemistry or infrared spectroscopy Additional Information All your information will be kept confidential according to EEO guidelines. 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 Our core beliefs - visit Employee engagement - visit Security Clearance None required. However, if your assignment is longer than 179 days cumulatively within a calendar year, you must go through the Personal Identity Verification process. This process includes completing an online background investigation form and receiving approval of the background check. (This process does not apply to foreign nationals.) 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. How to identify fake job advertisements Please be aware of recruitment scams where people or entities are misusing the name of Lawrence Livermore National Laboratory (LLNL) to post fake job advertisements. LLNL never extends an offer without a personal interview and will never charge a fee for joining our company. All current job openings are displayed on the Career Page under “Find Your Job” of our website. 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