Note: The job is a remote job and is open to candidates in USA. Pacific Northwest National Laboratory (PNNL) is a national laboratory conducting fundamental research across biological, computational, and other scientific disciplines. The PhD Intern will develop and evaluate mechanistic and statistical computational models, implement and benchmark algorithms, analyze software performance, and contribute to scientific software development and publications.
Responsibilities
- PNNL is seeking Ph.D. intern candidates with backgrounds in applied mathematics, statistics, or computational science, and experience with computational modeling in both mechanistic and statistical settings. Selected interns will contribute to modeling and simulation work at the interface of mechanistic and statistical models, with applications in biological and other scientific domains. Ideal candidates will have demonstrated ability to build software from mathematical specifications, with the behavior of each component explicitly specified rather than implicit in the implementation. Source code will be developed within sponsor-directed repositories as part of larger tools, some in team coding environments with rigorous software development practices
- Successful candidates will develop algorithmic solutions for directed modeling applications, including implementation, benchmarking, and analysis of software performance (both run-time and statistical), documentation, unit and functional testing, code reviews, and contributions to publications. Selected candidates will partner with project teams to review, refine, and improve code in line with requirements developed by the project team and sponsor
Skills
- Candidates must be currently enrolled/matriculated in a PhD program in computer science, applied mathematics, statistics, or a related field at an accredited college
- Minimum GPA of 3.0 is required
- In a Ph.D. program in computer science, applied mathematics, statistics, or a closely related field
- Scientific programming ability in Python and Julia, including experience with numerical performance work
- Experience with mechanistic or dynamical systems modeling, including systems that combine continuous evolution with discrete events
- Experience with Bayesian inference, probabilistic modeling, or parameter estimation for simulation-based models, including likelihood-free or simulation-based methods
- Experience with probabilistic programming frameworks, including extending or contributing to their internals rather than only applying them
- Experience with causal inference or counterfactual reasoning, including formal treatment of when interventions on a model are well-defined
- Familiarity with compositional approaches to program semantics, such as effect systems, handlers, or modular interpreters
- Familiarity with numerical solvers for differential equations or stochastic processes, and with performance-oriented numerical implementations
- Exposure to systems biology, metabolic modeling, bioprocess modeling, or other multi-scale scientific simulation
- Experience with reproducible computational workflows, version control, testing, and documentation of scientific software
- Publication record in probabilistic programming, causal inference, programming language semantics, scientific machine learning, or a related area
Benefits
- Health insurance
- Flexible work schedules
- Temporary Hourly employees are offered an employee assistance program and business travel insurance.
Company Overview
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