OverviewThe Physical and Computational Sciences Directorate (PCSD) researchers lead major R&D efforts in experimental and theoretical interfacial chemistry, chemical analysis, high energy physics, interfacial catalysis, multifunctional materials, and integrated high-performance and data-intensive computing.PCSD is PNNL’s primary steward for research supported by the Department of Energy’s Offices of Basic Energy Sciences, Advanced Scientific Computing Research, and Nuclear Physics, all within the Department of Energy's Office of Science.Additionally, Directorate staff perform research and development for private industry and other government agencies, such as the Department of Defense and NASA. The Directorate's researchers are members of interdisciplinary teams tackling challenges of national importance that cut across all missions of the Department of Energy.ResponsibilitiesThe Data Sciences & Machine Intelligence group in the Advanced Computing, Mathematics, and Data Division at PNNL seeks a post-doctorate researcher in Scientific Machine Learning (SciML) with a focus on physics-informed machine learning for dynamic system modeling and control, optimization based control, and differentiable predictive control. This role offers an exciting opportunity to contribute to transformative research at the intersection of control theory, machine learning, and energy sciences to create impact in complex, real-world systems, from building energy dynamics to renewable power, large scale networks and molecular simulations.We value diversity and encourage applicants from all backgrounds to bring their unique perspectives to our collaborative team environment. We welcome candidates who are passionate about experimentation and have a drive to apply computational methods and decision and control algorithms to complex, real-world dynamic systems. This role is ideal for candidates who thrive in a team-focused, interdisciplinary environment and who are interested in making meaningful contributions that impact diverse sectors of science and technology contributing to a more sustainable future.The successful candidate will leverage expertise in control systems and SciML to develop advanced physics-informed models that integrate domain-specific knowledge into machine learning frameworks, yielding interpretable, robust, and data-efficient solutions. The role emphasizes designing and implementing model-based and differentiable predictive control algorithms tailored for heterogeneous, large-scale dynamic systems, with a focus on real-world applications to experimental setups and control systems. We are looking for a highly motivated individual with a combination of advanced technical skills, interdisciplinary research experience, and a proactive approach to learning and innovation.Responsibilities Include

• Design physics-informed machine learning models that integrate physical laws to improve prediction accuracy and stability, benefiting fields like energy, transportation, robotics, materials science, and molecular dynamics.
• Develop model predictive control strategies that adapt to system changes, optimizing operations in applications like building energy management and renewable power generation.
• Develop differentiable predictive control systems using gradient-based methods for real-time adaptability in experimental settings.
• Lead algorithm development for predictive control, focusing on hyperparameter tuning to enhance efficiency, safety, and resilience across various applications.
• Develop and maintain high-quality software for machine learning and NLP projects, adhering to FAIR data principles for effective data management and reproducibility.
• Work with interdisciplinary teams to communicate findings effectively, contribute to peer-reviewed publications, and present research at scientific conferences.
• Support and mentor graduate and undergraduate interns, fostering their development in scientific research and data science.

QualificationsMinimum Qualifications:

• Candidates must have received a PhD within the past five years (60 months) or within the next 8 months from an accredited college or university.

Preferred Qualifications

• PhD in Computer Science, Electrical and Computer Engineering, Data Science or related field.
• Strong foundation in applied mathematics (e.g., linear algebra, dynamical systems, control theory, graph theory, topology, operator theory), and an understanding of machine learning for dynamic systems.
• Advanced understanding of control theory and machine learning for dynamic systems, with particular emphasis on scientific applications.
• Proficiency in programming languages (e.g., Python, MATLAB, Julia), experience with scientific software development, and familiarity with experimental systems.
• Knowledge of modern machine learning libraries (e.g., PyTorch, TensorFlow) and software version control (e.g., Git).
• Experience with advanced scientific deep learning techniques such as Neural ODEs, Physics-Informed Neural Networks (PINNs), Deep Operator Networks, and Graph Neural Networks.
• Expertise with a broader range of programming languages (e.g., C/C++, JavaScript) is a plus.
• Experience with dynamic visualization of high-dimensional datasets is a plus.
• Background in energy sciences (e.g., computational physics, computational chemistry, power systems) is a plus.
• Strong publication record in control, robotics, and machine learning conferences/journals (e.g., ACC, CDC, ECC, IROS, Automatica, IEEE TAC, NeurIPS, ICML, ICLR, AAAI)
• Experience releasing software tools is a plus.

Hazardous Working Conditions/EnvironmentNot applicableAdditional InformationNot applicableAbout PNNLPacific Northwest National Laboratory (PNNL) is a world-class research institution powered by a highly educated, diverse workforce committed to the values of Integrity, Creativity, Collaboration, Impact, and Courage. Every year, scores of dynamic, driven people come to PNNL to work with renowned researchers on meaningful science, innovations and outcomes for the U.S. Department of Energy and other sponsors; here is your chance to be one of them!At PNNL, you will find an exciting research environment and excellent benefits including health insurance, flexible work schedules and telework options. PNNL is located in eastern Washington State—the dry side of Washington known for its stellar outdoor recreation and affordable cost of living. The Lab’s campus is only a 45-minute flight (or ~3 hour drive) from Seattle or Portland, and is serviced by the convenient PSC airport, connected to 8 major hubs.Commitment to Excellence, Diversity, Equity, Inclusion, and Equal Employment OpportunityOur laboratory is committed to a diverse and inclusive work environment dedicated to solving critical challenges in fundamental sciences, national security, and energy resiliency. We are proud to be an Equal Employment Opportunity and Affirmative Action employer. In support of this commitment, we encourage people of all racial/ethnic identities, women, veterans, and individuals with disabilities to apply for employment.Pacific Northwest National Laboratory considers all applicants for employment without regard to race, religion, color, sex (including pregnancy, sexual orientation, and gender identity), national origin, age, disability, genetic information (including family medical history), protected veteran status, and any other status or characteristic protected by federal, state, and/or local laws.We are committed to providing reasonable accommodations for individuals with disabilities and disabled veterans in our job application procedures and in employment. If you need assistance or an accommodation due to a disability, contact us at [email protected].Drug Free WorkplacePNNL is committed to a drug-free workplace supported by Workplace Substance Abuse Program (WSAP) and complies with federal laws prohibiting the possession and use of illegal drugs.If you are offered employment at PNNL, you must pass a drug test prior to commencing employment. PNNL complies with federal law regarding illegal drug use. Under federal law, marijuana remains an illegal drug. If you test positive for any illegal controlled substance, including marijuana, your offer of employment will be withdrawn.HSPD-12 PIV Credential RequirementIn accordance with Homeland Security Presidential Directive 12 (HSPD-12) and Department of Energy (DOE) Order 473.1A, new employees are required to obtain and maintain a HSPD-12 Personal Identity Verification (PIV) Credential. To obtain this credential, new employees must successfully complete and pass a Federal Tier 1 background check investigation. This investigation includes a declaration of illegal drug activities, including use, supply, possession, or manufacture within the last year. This includes marijuana and cannabis derivatives, which are still considered illegal under federal law, regardless of state laws.Mandatory RequirementsPlease be aware that the Department of Energy (DOE) prohibits DOE employees and contractors from having any affiliation with the foreign government of a country DOE has identified as a “country of risk” without explicit approval by DOE and Battelle. If you are offered a position at PNNL and currently have any affiliation with the government of one of these countries, you will be required to disclose this information and recuse yourself of that affiliation or receive approval from DOE and Battelle prior to your first day of employment.Rockstar RewardsEmployees and their families are offered medical insurance, dental insurance, vision insurance, health savings account, flexible spending accounts, basic life insurance, disability insurance*, employee assistance program, business travel insurance, tuition assistance, supplemental parental bonding leave**, surrogacy and adoption assistance, and fertility support. Employees are automatically enrolled in our company funded pension plan* and may enroll in our 401k savings plan. Employees may accrue up to 120 vacation hours per year and may receive ten paid holidays per year.

• Research Associates excluded.
• Once eligibility requirements are met.

Click Here For Rockstar RewardsNotice to ApplicantsPNNL lists the full pay range for the position in the job posting. Starting pay is calculated from the minimum of the pay range and actual placement in the range is determined based on an individual’s relevant job-related skills, qualifications, and experience. This approach is applicable to all positions, with the exception of positions governed by collective bargaining agreements and certain limited-term positions which have specific pay rules.As part of our commitment to fair compensation practices, we do not ask for or consider current or past salaries in making compensation offers at hire. Instead, our compensation offers are determined by the specific requirements of the position, prevailing market trends, applicable collective bargaining agreements, pay equity for the position type, and individual qualifications and skills relevant to the performance of the position.Minimum SalaryUSD $69,000.00/Yr.Maximum SalaryUSD $119,100.00/Yr.