Multi-Source Data Fusion Research Associate

utaustin; United States; On-site; USD 90,000 - 120,000; Full time

Job summary

Applied Research Laboratories at The University of Texas at Austin seeks a Multi-Source Data Fusion Research Associate on the Pickle Research Campus to advance multi-source data fusion, environmental and geospatial modeling, and ML within SGL. You will design and evaluate fusion algorithms, develop digital twin and multi-agent simulations, and support decision-making with predictive analytics.

The role requires a PhD in a relevant field, demonstrated software experience for scientific

Qualifications

• PhD in engineering, computational science, physics, mathematics, geospatial or related field.
• 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.

Responsibilities

• Perform high-level independent research in multi-source data fusion, environmental and geospatial modeling, ML, and digital twin/MAS relevant to SGL.
• Present and publish research internally and externally; participate in reviews and interface meetings.
• Develop algorithms and designs for data fusion and modeling applications; employ statistical estimation and geospatial modeling.
• 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 to generate predictions and support decision making.
• Design experiments and analyses to gauge performance of fusion, classification, routing, and decision support methods.
• Implement software algorithms in a collaborative environment and document interfaces and tests.
• Advance knowledge of multi-source data fusion, estimation, ML, environment modeling, and software development principles.
• Provide guidance to staff and student employees and identify new research directions.
• Occasional travel to support meetings.

Skills

Data fusion

Machine learning

Geospatial modeling

Statistical estimation

Technical communication

Education

PhD in engineering or related field

Tools

Python

MATLAB

Job description

Job Posting Title: Multi-Source Data Fusion Research Associate

Hiring Department: Applied Research Laboratories

Position Open To: All Applicants

Weekly Scheduled Hours: 40

FLSA Status: Exempt from FLSA

Earliest Start Date: Immediately

Position Duration: Expected to Continue

Location: PICKLE RESEARCH CAMPUS

Job Details

Purpose

Applied Research Laboratories is a University Affiliated Research Center at The University of Texas at Austin, located on the Pickle Research Campus in North Austin. Through fundamental research, innovative science, and applied engineering, ARL:UT makes significant contributions in support of national security.

Within ARL:UT, the Space and Geophysics Laboratory (SGL) engages in research in electromagnetic propagation, geo-positioning, and remote sensing. The lab monitors the long-term performance of GPS, addresses the specification of government geodetic-quality GPS receivers, and designs and executes government tests for industry-produced receivers.

We are seeking a Research Associate to provide multi-source data fusion, environmental and geospatial modeling, and machine learning expertise and scientific rigor across a portfolio of technologies germane to interests of SGL listed above in order to effect improvements to these interests and advance the state-of-the-art in these areas. The successful candidate will support the design, development, and independent evaluation of multi-source data fusion, environmental modeling, and digital twin / multi-agent simulation capabilities, including object detection, classification and tracking, route prediction, and decision support functions.

Responsibilities

• Perform high-level independent research in multi-source data fusion, environmental and geospatial modeling, machine learning, and digital twin / multi-agent simulation relevant to the interests of SGL.
• Present and publish your research, both internally and externally. This may involve participating in project reviews and technical interface meetings to present designs and test results to sponsors and other external organizations, as well as publishing work in scientific journals and presenting work at conferences.
• Contribute to the first principles problem formulations, algorithm development, systems designs, and software implementations for specialized data fusion and modeling applications, employing 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 and movement patterns and to simulate courses of action.
• Contribute to digital twin and multi-agent simulation capabilities that synthesize historical patterns, environmental models, and activity data to generate predictions, identify areas of interest and support decision making.
• Design experiments and corresponding analyses to gauge the performance of fusion, object classification and tracking, route prediction, and decision support methods and develop recommendations to effect improvements.
• Implement software algorithms in a collaborative environment. Work closely with team members to define interfaces between program components. Document implementations thoroughly and clearly. Define, implement, and execute test cases for program functions.
• Further develop knowledge of multi-source data fusion, statistical estimation, machine learning, environmental modeling, and software development principles.
• Provide guidance and assistance to other full time employees and student employees.
• Provide technical leadership and identify new directions for research.
• Other related functions as assigned and occasional travel to support meetings.

Required Qualifications

• PhD in engineering, computer science, computational science, physics, mathematics, oceanographic science, earth science, geospatial science, or 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.

Applicant must have a dynamic skill set, willing to work with new technologies, be highly organized and capable of planning and coordinating multiple tasks and managing their time. The position will require attention to detail, effective problem solving skills and excellent judgment. Ability to work independently with sensitive and confidential information, maintain a professional demeanor, work as a team member without daily supervision and effectively communicate with various groups of clients. Able to work under pressure and accept supervision. Regular and punctual attendance.

US Citizen. Applicant selected wi