Multi-Source Data Fusion Research Associate

University of Texas at Austin•Austin, TX

1d•$120,000 - $165,000•Onsite

About The Position

Applied Research Laboratories (ARL:UT), a University Affiliated Research Center at The University of Texas at Austin, conducts fundamental research, innovative science, and applied engineering in support of national security. The Space and Geophysics Laboratory (SGL) conducts research in electromagnetic propagation, geo-positioning, and remote sensing, including monitoring GPS performance, addressing government geodetic-quality GPS receiver specifications, and testing industry-produced receivers. The Research Associate will provide expertise and scientific rigor in multi-source data fusion, environmental and geospatial modeling, and machine learning across technologies relevant to SGL. The role supports the design, development, and independent evaluation of data fusion, environmental modeling, and digital twin/multi-agent simulation capabilities, including object detection, classification and tracking, route prediction, and decision support. The position is based at the Pickle Research Campus in North Austin and is expected to continue.

Requirements

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.
U.S. citizenship.
Must meet eligibility requirements for access to classified information at the level appropriate to project requirements, subject to a government security investigation.
Ability and willingness to work with new technologies; strong organization, planning, coordination, and time-management skills.
Attention to detail, effective problem-solving skills, and sound judgment.
Ability to handle sensitive and confidential information independently, maintain a professional demeanor, work as part of a team without daily supervision, communicate effectively with varied client groups, work under pressure, accept supervision, and maintain regular and punctual attendance.

Nice To Haves

Experience developing and evaluating multi-source data fusion systems, including detection, correlation, and classification refinement.
Experience with object detection, classification, and tracking using varied data sources.
Experience developing and applying environmental models, such as oceanographic, meteorological, terrain, or bathymetric models, for prediction, simulation, and suitability analysis.
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 writing proposals for and leading externally funded research projects.
Proficiency in programming languages such as Python, C/C++, FORTRAN, or similar.
Experience developing on Linux and Windows platforms, including terminal-based environments such as bash, version control such as Git and GitHub/GitLab, and cloud services such as AWS.
Proficiency with LaTeX.
Minimum cumulative GPA of 3.0.

Responsibilities

Perform independent research in multi-source data fusion, environmental and geospatial modeling, machine learning, and digital twin/multi-agent simulation relevant to SGL.
Present and publish research internally and externally, including participation in project reviews and technical interface meetings, presentation of designs and test results, scientific journal publications, and conference presentations.
Contribute to first-principles problem formulations, algorithm development, system designs, and software implementations for specialized data fusion and modeling applications.
Develop multi-source data fusion algorithms to improve detection, correlation, and classification.
Develop environmental suitability models to predict routes and movement patterns and simulate courses of action.
Contribute to digital twin and multi-agent simulation capabilities that combine historical patterns, environmental models, and activity data to make predictions, identify areas of interest, and support decision-making.
Design experiments and analyses to evaluate fusion, object classification and tracking, route prediction, and decision-support methods, and recommend improvements.
Implement software algorithms collaboratively; define component interfaces with team members; document implementations; and define, implement, and execute test cases.
Further develop knowledge of data fusion, statistical estimation, machine learning, environmental modeling, and software development principles.
Provide guidance and assistance to full-time employees and student employees.
Provide technical leadership and identify new research directions.
Perform other related functions as assigned and travel occasionally to support meetings.