Computational Materials Scientist - Postdoctoral Researcher

Computational Materials Scientist - Postdoctoral Researcher Full-time Organization: Physical and Life Sciences Category: Postdoctoral/Fellowship Employee Referral Bonus: Not applicable Job Code 1: PDS.1 Post-Dr Research Staff 1 Pre-Employment Drug Test: Required for external applicant(s) selected for this position (includes testing for use of marijuana) Pre-Placement Medical Exam: Not applicable Position Type: Post Doctoral Security Clearance: Anticipated DOE Q clearance (requires U.S. citizenship and a federal background investigation) Join us and make YOUR mark on the World! Lawrence Livermore National Laboratory (LLNL) has turned bold ideas into world-changing impact advancing science and technology to strengthen U.S. security and promote global stability. Our mission spans four critical national security areas nuclear deterrence, threat preparedness, energy security, and multi-domain defense empowering teams to take on the toughest challenges of today and tomorrow. With a culture built on innovation and operational excellence, LLNL is a place where your expertise can make a real impact. We have an opening for a Postdoctoral Researcher to work in the field of computational materials science and actively participate in research dedicated to the discovery of new structural alloys for extreme environments. You will be involved in research to implement methods for materials design and process development. You will be a creative force in the integration and application of thermodynamic and kinetic models into an alloy design framework (including material properties models) that uses numerical optimization methods to rapidly screen for promising compositions over vast multi-component phase spaces. This position is in the Actinide and Lanthanide Science group within the Materials Science Division. In this role you will Independently develop multicomponent thermodynamic and kinetic databases for inorganic (metal, oxide, carbide, hydride, etc.) systems. Incorporate new Integrated Computational Materials Engineering (ICME) microstructure evolution models and resulting property predictions (ranging from analytical to machine learning surrogate models) in LLNL’s Materials Acceleration Platform that is deployed on LLNL’s High Performance Computing infrastructure. Develop uncertainty quantification (UQ) and propagation methods into ICME approaches. Interface with experimentalists in design of experiment and material development campaigns. Work both independently and collaborate with others in a multidisciplinary team environment to accomplish program goals. Publish research results in peer-reviewed scientific journals and present results at external conferences, seminars, and technical meetings. Perform other duties as assigned. Qualifications Ability to secure and maintain a U.S. DOE Q-level security clearance which requires U.S. citizenship. PhD in materials science, metallurgy, condensed matter physics, applied math, or a closely related field. Experience and knowledge in at least three of the following areas: CALPHAD, microstructure and property modeling, uncertainty quantification and propagation, ICME, alloy design, design of experiments. Demonstrated ability to independently develop or make significant contributions to scientific research software. Experience with commercial (Thermo-Calc, Pandat, or FactSage) or open source (PyCalphad, Thermochimica, or OpenCalphad) computational thermodynamics software to perform CALPHAD database development. Experience in at least twoof the following metallurgy topics: thermodynamics, phase stability, phase transformations, defect structures, solidification, or thermo-mechanical processing. Proficient verbal and written communication skills as reflected in effective presentations at meetings and a demonstrated strong publication record. Initiative and interpersonal skills with desire and ability to work in a collaborative, multidisciplinary team environment. Qualifications We Desire Experience with artificial intelligence (AI) and/or machine learning (ML) methods. Experience with algorithms relevant for materials design and discovery (Bayesian Optimization, black-box optimization, gradient-based optimization). Experience parameterizing or developing numerical methods for thermodynamic and/or kinetic modeling of phase transformations and microstructure evolution (e.g. CALPHAD, Kampmann-Wagner numerical method, phase field, cellular automata, Monte Carlo method, continuum models). Direct experience with alloy synthesis, processing, and/or characterization; or extensive experience collaborating with experimental colleagues. Pay Range $123,048 Annually This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting. An employee's position within the salary range will be based on several factors including, but not limited to, specific competencies, relevant education, qualifications, certifications, experience, skills, seniority, geographic location, performance, and business or organizational needs. All your information will be kept confidential according to EEO guidelines. 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. This position requires a Department of Energy (DOE) Q-level clearance.If you are selected, wewill initiate a Federal background investigation to determine if youmeet 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. Why Lawrence Livermore National Laboratory? Included in 2026Best Places to Work by Glassdoor! Flexible schedules (*depending on project needs) 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. 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 useand/or possession ofmobile devices in Limited Areas. Depending on your job duties, you may be required to work in a Limited Area whereyou are not permitted to have a personal and/or laboratory mobile devicein your possession. This includes, but not limited to cell phones, tablets, fitness devices, wireless headphones, and other Bluetooth/wireless enabled devices. Ifyou useamedical device, whichpairs with a mobile device,you must still follow the rules concerningthe mobile device in individual sections within Limited Areas. Sensitive Compartmented Information Facilities requireseparate 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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