Pacific Northwest National Laboratory
Post Doctorate RA - Scientific Machine Learning (SciML)
Pacific Northwest National Laboratory, Providence, Rhode Island, us, 02912
Pacific Northwest National Laboratory
Post Doctorate RA - Scientific Machine Learning (SciML)
Providence, Rhode Island
The 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. 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.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.About 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.
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Post Doctorate RA - Scientific Machine Learning (SciML)
Providence, Rhode Island
The 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. 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.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.About 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.
#J-18808-Ljbffr