Nuclear Security Post-Doc The Computational Nuclear Engineering Research Group (CNERG) is participating in a pair of research consortia supported by the Nuclear Non-Proliferation R&D Office of the NNSA. Consortium for Enabling Technology and Innovation CNERG is a leading participant in Data Science research thrust area of the Consortium for Enabling Technology and Innovation that will integrate cutting edge algorithms for machine learning and data fusion with evolving data streams to enhance the ability to identify misuse of nuclear materials and facilities. The collaboration includes researchers from the U. Wisconsin-Madison, U. Michigan, Duke, MIT, Texas A&M, Georgia Tech, Washington State, & U. Hawaii, as well as a number of DOE National Laboratories. The four over-arching research areas within this thrust are: Framework for rapid evaluation of algorithms and data streams Fundamental advances in data science algorithms Machine-guided learning and data collection Incorporation of novel data streams CNERG researchers will lead the application of these technologies to problems relevant to the nuclear non-proliferation mission, with a particular focus on the development of the integrating framework. Consortium for Monitoring, Technology and Verification CNERG is also a participant in the Signals and Source Terms research thrust area of the Consortium for Monitoring, Technology and Verification that will provide insight into the set of measurable quantities at various stages in the processing and handling of nuclear materials. CNERG researchers develop computational models of nuclear facilities with sufficient fidelity to simulate the time-varying nature of such measurable quantities as those facilities engage in normal and off-normal activities. Post-doctoral Activities CNERG is seeking a qualified individual(s) to: assist in the application of modern data analytics algorithms to nuclear non-proliferation data sets, through the development of a framework that integrates both new and existing algorithms and data streams, and/or provide leadership in the development and validation of such computational models. Working in collaboration with other consortium partners, the primary measurables of interest will be identified and the models will be improved to increase the reliability of simulating those measurable quantities. Degree and area of specialization PhD in Computer Science, Nuclear Engineering, Electrical Engineering, Statistics, Industrial Engineering, Physics, or related field Required experience Demonstrated experience with development and testing of machine learning and/or data fusion algorithms and/or software models for nuclear systems, particularly in the areas of radiation transport and nuclide inventory tracking Some experience with components of a modern team-based software development processes: version control (e.g. git) bug/issue tracking (e.g. github) test driven development (e.g. google test and pytest) automatic documentation (e.g. doxygen and sphinx) build & configuration management (e.g. cmake, make) Strong oral and written communication skills Desired Experience Demonstrated experience with the application of machine learning and/or data fusion algorithms to real-world problems Experience working across disciplinary boundaries for the application of data analytics algorithms Familiarity with monitoring and safeguards of nuclear fuel cycle facilities Experience with nuclear fuel cycle simulation Experience with contemporary machine learning and/or data fusion algorithms Experience with C++ and/or python programming languages Specific Duties Identify and implement algorithms for use in nuclear non-proliferation applications, or surrogate applications as defined by consortium partners Develop systems for integration of various data streams and various algorithms to support real-world applications of algorithms Identify safeguards and monitoring approaches for nuclear fuel cycle facilities Develop and improve nuclear fuel cycle facility models to support simulation of those safeguards and monitoring approaches Mentor and advise graduate student and undergraduate researchers Collaborate with multiple research groups across consortium Author publications for refereed journals and important conference proceedings Salary: $50,000-70,000 depending on background and experience Start: ASAP U.S. citizenship or permanent residency status is required by the program sponsor. To apply, please send a letter of interest and CV to Paul Wilson at paul.wilson@wisc.edu For further questions please contact Paul Wilson at paul.wilson@wisc.edu