Postdoctoral Research Associate II

The Space4 Center at the University of Arizona seeks a Postdoctoral Research Associate II to conduct advanced research in Space Situational/Domain Awareness (SSA/SDA), with emphasis on nonlinear state estimation, multi-target tracking, and autonomous guidance, navigation, and control (GNC) systems. The successful candidate will develop and validate Bayesian and non-Gaussian estimation algorithms, data assimilation methods, and tracking frameworks for space and hypersonic applications, integrating heterogeneous sensor data (EO, radar, IMU, LiDAR) into robust orbit determination and relative navigation pipelines. The position spans theoretical algorithm development, high-fidelity simulation, and end-to-end system implementation, with transition to mission-relevant applications. The postdoc will publish in leading journals, collaborate with government and industry partners, and contribute to proposals aligned with Space4 programs.

Outstanding U of A benefits include health, dental, vision, and life insurance; paid vacation, sick leave, and holidays; U of A/ASU/NAU tuition reduction for the employee and qualified family members; access to U of A recreation and cultural activities; and more!

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• Design and implement nonlinear, non-Gaussian state estimation and multi-target tracking algorithms (e.g., Kalman/particle filters, Gaussian mixture filters, random finite set methods, MCMC-based approaches) for SSA/SDA and aerospace applications. Develop novel Bayesian frameworks for orbit determination, relative navigation, and maneuvering target tracking under uncertainty.
• Develop and integrate estimation algorithms within closed-loop GNC
  systems for space and hypersonic applications, including relative navigation,
  rendezvous and proximity operations (RPOD), and autonomous targeting. Evaluate
  performance in high-fidelity simulation.
• Build and maintain high-fidelity simulation environments and Monte Carlo frameworks to validate estimation and tracking algorithms. Perform statistical analysis of algorithm performance, uncertainty quantification, and sensitivity studies using synthetic and real-world data.
• Develop reproducible, scalable software in Python, MATLAB, and C++ for research and operational transition. Maintain version-controlled repositories, documentation, and workflows aligned with Space4 data pipelines and partner integration requirements.
• Lead and contribute to journal and conference publications (e.g., AIAA, IEEE, JGCD). Collaborate with interdisciplinary teams across academia, government, and industry. Support proposal development and technical reporting aligned with Space4 strategic initiatives.

Knowledge, Skills and Abilities:

• Ability to work in
  multidisciplinary teams and communicate technical results effectively.
• Strong programming skills in
  Python and/or MATLAB, with experience in scientific computing and simulation.
• Ability to design and
  analyze data assimilation frameworks for high-dimensional dynamical systems.
• Strong skills in modeling,
  simulation, and Monte Carlo analysis for aerospace systems.
• Ability to integrate
  estimation algorithms into closed-loop GNC architectures.
• Strong analytical thinking
  and problem-solving skills applied to complex dynamical systems.
• Ability to communicate
  complex technical concepts clearly in publications, presentations, and sponsor
  briefings.
• Knowledge of sensor fusion
  involving EO, radar, IMU, or LiDAR systems.
• Ability to mentor students
  and collaborate in interdisciplinary research environments.
• Background
  in nonlinear and non-Gaussian estimation techniques and data assimilation.
• Familiarity
  with hypersonic systems, GNC, or aerospace autonomy.
• Nonlinear/non-Gaussian
  estimation, MTT, PHD filters.
• Deep
  understanding of Bayesian estimation theory, stochastic processes, and
  statistical inference.
• Proficiency
  in scientific programming (Python, MATLAB, C++) and software engineering
  best practices (Git, testing, documentation).

• Must have a Ph.D. in Aerospace
  Engineering, Systems Engineering, Electrical Engineering, Applied Mathematics,
  or a closely related field upon hire.

• Experience with multi-target tracking (MTT) and RFS-based filters (e.g., PHD, JPDA).
• Experience in orbit determination, SSA/SDA, or relative navigation.
• Experience with Monte Carlo validation, uncertainty quantification, and statistical analysis.
• Experience working with government or industry aerospace programs.
• Expertise in state estimation, stochastic processes, or Bayesian inference.
• Experience with multi-sensor data fusion and tracking algorithms (e.g., Kalman filters, particle filters, Gaussian mixtures, or RFS-based methods).
• Evidence of independent research through peer-reviewed publications.
• Expertise in nonlinear filtering and multi-target tracking algorithms, including Gaussian mixture and RFS-based approaches.
• Experience with sensor fusion across heterogeneous data sources.