Computational Science PhD Intern
Pacific Northwest National Laboratory
Job Overview
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Job Description
Overview
At PNNL, our core capabilities are divided among major departments that we refer to as Directorates within the Lab, focused on a specific area of scientific research or other function, with its own leadership team and dedicated budget.Our Science & Technology directorates include National Security, Earth and Biological Sciences, Physical and Computational Sciences, and Energy and Environment. In addition, we have an Environmental Molecular Sciences Laboratory, a Department of Energy, Office of Science user facility housed on the PNNL campus.The Physical and Computational Sciences Directorate's (PCSD’s) strengths in experimental, computational, and theoretical chemistry and materials science, together with our advanced computing, applied mathematics and data science capabilities, are central to the discovery mission we embrace at PNNL. But our most important resource is our people—experts across the range of scientific disciplines who team together to take on the biggest scientific challenges of our time.The Advanced Computing, Mathematics, and Data Division (ACMDD) focuses on basic and applied computing research encompassing artificial intelligence, applied mathematics, computing technologies, and data and computational engineering. Our scientists and engineers apply end-to-end co-design principles to advance future energy-efficient computing systems and design the next generation of algorithms to analyze, model, understand, and control the behavior of complex systems in science, energy, and national security.
Responsibilities
- Support development of simulation tools for chemical computing and reservoir computing in Python.
- Help model dynamical systems (ODEs/PDEs) and run computational experiments to evaluate proposed approaches.
- Assist with implementing and comparing methods, collecting results, and analyzing performance and error.
- Contribute to building reusable code components and workflows for repeatable experiments.
- Document methods and findings and communicate progress through short technical summaries and presentations.
Qualifications
Minimum Qualifications:
- Candidates must be currently enrolled/matriculated in a PhD program at an accredited college.
- Minimum GPA of 3.0 is required.
Preferred Qualifications:
- PhD Degree in a computational science, Mathematics, Applied Mathematics, Physics, Chemistry, Computer Science or related field.
- Desired Interest in mathematics and/or computer science.
- Experience with python computing language.
- Familiarity with differential equations, dynamical systems, linear algebra.
- Appreciation of chemistry and/or biochemistry, thermodynamics.
About PNNL
Pacific 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, and flexible work schedules. 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.
Key skills/competency
- Python
- Simulation
- Chemical Computing
- Reservoir Computing
- Dynamical Systems
- ODEs/PDEs
- Computational Experiments
- Data Analysis
- Scientific Programming
- Linear Algebra
How to Get Hired at Pacific Northwest National Laboratory
- Research PNNL's culture: Study their mission, values, recent news, and employee testimonials on LinkedIn and Glassdoor.
- Tailor your resume: Highlight computational science, Python proficiency, and modeling experience for specific job requirements.
- Showcase research impact: Emphasize your academic projects and contributions, detailing their scientific significance and outcomes.
- Prepare for technical questions: Review core concepts in differential equations, linear algebra, dynamical systems, and Python programming.
- Demonstrate collaboration: Be ready to discuss experiences in multidisciplinary teams and your ability to communicate complex ideas.
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