PhD Intern - Computational Biology Advanced Modeling

Pacific Northwest National Laboratory•UNAVAILABLE, UNAVAILABLE

2d•Onsite

About The Position

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.

Requirements

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.

Nice To Haves

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.

Responsibilities

Develop algorithmic solutions for directed modeling applications.
Implement algorithmic solutions.
Benchmark algorithmic solutions.
Analyze software performance (both run-time and statistical).
Document code.
Perform unit and functional testing.
Conduct code reviews.
Contribute to publications.
Partner with project teams to review, refine, and improve code in line with requirements developed by the project team and sponsor.