Pacific Northwest National Laboratory
Post Doctorate RA - Scientific Machine Learning (SciML)
Pacific Northwest National Laboratory, Juneau, Alaska, us, 99812
Pacific Northwest National Laboratory
Post Doctorate RA - Scientific Machine Learning (SciML)
Location: Juneau, AlaskaThe 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.ResponsibilitiesDesign 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.Additional InformationPacific 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.
#J-18808-Ljbffr
Post Doctorate RA - Scientific Machine Learning (SciML)
Location: Juneau, AlaskaThe 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.ResponsibilitiesDesign 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.Additional InformationPacific 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.
#J-18808-Ljbffr