Multi-Source Data Fusion Research Associate

About the Role

Applied Research Laboratories (ARL) at The University of Texas at Austin is seeking a Research Associate to contribute to advancements in multi-source data fusion, environmental and geospatial modeling, and machine learning. This role supports the Space and Geophysics Laboratory (SGL), which focuses on research in electromagnetic propagation, geo-positioning, and remote sensing, including GPS performance monitoring and receiver testing.

Key Responsibilities

• Perform high-level independent research in multi-source data fusion, environmental and geospatial modeling, machine learning, and digital twin / multi-agent simulation.
• Support the design, development, and evaluation of these capabilities, including object detection, classification, and tracking, route prediction, and decision support functions.
• Present and publish research findings internally and externally, participating in project reviews and technical interface meetings.
• Contribute to problem formulation, algorithm development, systems design, and software implementation using expertise in statistical estimation, geospatial and environmental modeling, machine learning, and sensor phenomenology.
• Develop multi-source data fusion algorithms to improve detection, correlation, and classification.
• Develop environmental suitability models to predict routes, movement patterns, and simulate courses of action.
• Contribute to digital twin and multi-agent simulation capabilities for generating predictions and supporting decision-making.
• Design experiments and analyses to evaluate the performance of fusion, object classification, tracking, route prediction, and decision support methods.
• Implement software algorithms collaboratively, defining interfaces, documenting implementations, and defining/executing test cases.
• Further develop knowledge in multi-source data fusion, statistical estimation, machine learning, environmental modeling, and software development principles.
• Provide guidance and assistance to other employees.
• Provide technical leadership and identify new research directions.
• Other related functions as assigned and occasional travel.

Requirements & Tech Stack

• PhD in engineering, computer science, computational science, physics, mathematics, oceanographic science, earth science, geospatial science, or a related discipline.
• Experience developing software for scientific applications.
• Experience developing data fusion, statistical estimation, machine learning, or environmental/geospatial modeling algorithms.
• Demonstrated record of authoring peer-reviewed scientific publications.
• Demonstrated record of presenting scientific content to technical and non-technical audiences.
• Ability to work with new technologies, be highly organized, manage multiple tasks, and prioritize time.
• Attention to detail, effective problem-solving skills, and excellent judgment.
• Ability to work independently with sensitive information, maintain a professional demeanor, work as a team member without daily supervision, and communicate effectively.
• Ability to work under pressure and accept supervision.
• Regular and punctual attendance.
• US Citizen.
• Subject to government security investigation and eligibility for classified information.

Preferred Qualifications

• Experience developing and evaluating multi-source data fusion systems.
• Experience with object detection, classification, and tracking using various data sources.
• Experience developing and applying environmental models (e.g., oceanographic, meteorological, terrain, bathymetric).
• Experience applying machine learning to detection, classification, and prediction problems.
• Experience with digital twin architectures, agent-based modeling, and simulation of operational courses of action.
• Experience with route prediction, behavior modeling, and movement pattern analysis.
• Experience designing, implementing, and deploying robust scientific software.
• Experience employing statistical estimation techniques.
• Experience with high-performance computing.
• Experience with geospatial data processing and Geographic Information Systems (GIS) tools.
• Eligibility for immediate access to classified information.
• Experience in writing proposals and leading externally-funded research projects.
• Proficiency with programming languages such as Python, C/C++, FORTRAN, or similar.
• Experience developing on Linux and Windows platforms, including terminal-based environments (bash), version control (Git and GitHub/GitLab), and cloud services (AWS).
• Proficiency with LaTeX.
• Minimum cumulative GPA of 3.0.