What Your Job Will Be LikeWe are seeking a postdoctoral researcher with expertise in machine learning (ML), molecular dynamics (MD) simulations, and computational materials science to join our multidisciplinary team focused on advancing complex solvent-based separation systems. In this role, you will collaborate closely with synthetic chemists and experimentalists to develop fundamental insights into separations processes within multi-state materials systems.
On any given day, you may be called on to:
- Design, perform, and analyze molecular scale simulations of nanoporous and/or liquid phase materials
- Leverage existing machine learning or artificial intelligence tools to create and analyze structure-property relationships based on experimental and modeling data
- Conduct work in a multi-disciplinary team environment by working closely with experimental and synthetic chemists
- Write research manuscripts and attend & present at technical conferences and/or program meetings
Due to the nature of the work, the selected applicant must be able to work onsite. This position is located in Albuquerque, NM, and the annual salary for this position is $100,500.
Qualifications We Require- PhD in materials science and engineering, chemistry, chemical engineering, physics, applied mathematics, computer science, data science, or other relevant, related field
- Demonstrated experience building, evaluating, and applying machine learning models (such as probabilistic surrogate models, active learning, and/or ML-enhanced atomistic potentials) to experimental and/or computational datasets
- Experience with molecular dynamics simulations and/or integration of ML methods with simulation tools
- Strong record of technical accomplishments and written communication, as evidenced by peer-reviewed publication(s)
Qualifications We Desire- Strong verbal and written communication skills, with a proven ability to collaborate effectively in multidisciplinary team environments
- Familiarity with advanced ML/AI techniques such as generative adversarial networks, Bayesian deep learning, graph neural networks, atomic cluster expansion descriptors, or similar
- Experience developing machine-learning models to elucidate structure-property or structure-function relationships in porous materials, metal-organic frameworks (MOFs), porous liquids, or complex solvent systems (e.g., ionic liquids, deep eutectic solvents)
- Proficiency in programming and data management, particularly with Python-based data science tools, and familiarity with scientific data infrastructure and cloud computing environments
- Proven ability to build effective working relationships and contribute creative solutions within team settings
- Flexibility to work in a hybrid work environment, as required
- Ability to obtain and maintain DOE Q-level security clearance
About Our TeamSandia's Geomechanics and Geochemistry Department advances fundamental scientific understanding and develops innovative capabilities to address complex challenges in subsurface science, energy, national security, and environmental sustainability. The department integrates experimental investigations, advanced characterization, computational modeling, and engineering applications across multiple spatial and temporal scales.
Sandia's geomechanics research addresses cross-cutting challenges in rock mechanics, rock mass characterization, deformation and failure processes, numerical modeling and simulation, material model validation, and subsurface monitoring. Integrating laboratory experiments, field observations, and computational simulations, key research areas include characterization of natural fracture systems; investigation and modeling of rock deformation and failure mechanisms; determination of thermomechanical, hydrological, and transport properties of rocks and fractures, including coupled processes; scaling of laboratory measurements to field conditions; in situ stress measurements and evaluation of subsurface boundary conditions; and laboratory- and field-scale validation of predictive models. These capabilities support applications in subsurface energy systems, critical mineral recovery, nuclear waste isolation, and national security.
Sandia's geochemistry research focuses on understanding molecular-scale processes and chemical interactions at mineral¿fluid interfaces, including how chemical environments influence mineral reactivity, interfacial transport, and material behavior. Core capabilities span three complementary areas: (1) molecular-scale modeling and simulation, (2) experimental geochemistry, and (3) applied environmental and chemical engineering. By integrating molecular-level insights, laboratory experiments, and multiscale computational modeling, researchers develop and validate predictive models of complex geochemical processes. These capabilities support a broad range of applications, including critical mineral extraction and separation, subsurface energy resource recovery, nuclear waste management, environmental remediation, nuclear weapons applications, and contaminant detection and removal.
Posting DurationThis posting will be open for application submissions for a minimum of three (3) calendar days, including the 'posting date'. Sandia reserves the right to extend the posting date at any time.
Security ClearanceThis position does not currently require a Department of Energy (DOE) security clearance.
Position InformationThis postdoctoral position is a temporary position for up to one year, which may be renewed at Sandia's discretion up to five additional years. The PhD must have been conferred within five years prior to employment.
Individuals in postdoctoral positions may bid on regular Sandia positions as internal candidates, and in some cases may be converted to regular career positions during their term if warranted by ongoing operational needs, continuing availability of funds, and satisfactory job performance.
