Multi-Source Data Fusion Research Associate

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 will be subject to a government security investigation and must meet eligibility requirements for access to classified information at the level appropriate to the project requirements of the position.

Preferred Qualifications Experience developing and evaluating multi-source data fusion systems, including detection, correlation, and classification refinement. Experience with object detection, classification, and tracking using various data sources. Experience developing and applying environmental models (e.g., oceanographic, meteorological, terrain, bathymetric) 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 in writing proposals for 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 (such as AWS). Proficiency with LaTeX. Minimum cumulative GPA of 3.0.

General Notes

An agency designated by the federal government handles the investigation as to the requirement for eligibility for access to classified information. Factors considered during this investigation include but are not limited to allegiance to the United States, foreign influence, foreign preference, criminal conduct, security violations, drug involvement, the likelihood of continuation of such conduct, etc.

Please mark "yes" on the application question that asks if additional materials are required. Failure to attach all additional materials listed below may result in a delay in application processing.

Visit our website ( ) for additional information about Applied Research Laboratories.

UT Austin offers a competitive benefits package that includes: - 100% employer-paid basic medical coverage - Retirement contributions - Paid vacation and sick time - Paid holidays

Please visit

Human Resources (HR) website

to learn more about the total benefits offered.

Salary Range

$120,000-$165,000+/negotiable depending on qualifications

Working Conditions

Standard office conditions Repetitive use of a keyboard at a workstation Use of manual dexterity Some weekend, evening and holiday work Possible interstate/intrastate travel

Required Materials

Resume/CV 3 work references with their contact information; at least one reference should be from a supervisor Letter of interest Unofficial college transcripts

Important

for applicants who are NOT current university employees or contingent workers:

You will be prompted to submit your resume the first time you apply, then you will be provided an option to upload a new Resume for subsequent applications. Any additional Required Materials (letter of interest, references, etc.) will be uploaded in the Application Questions section; you will be able to multi-select additional files. Before submitting your online job application, ensure that ALL Required Materials have been uploaded. Once your job application has been submitted, you cannot make changes.

Important for Current university employees and contingent workers:

As a current university employee or contingent worker, you MUST apply within Workday by searching for Find UT Jobs. If you are a current University employee, log-in to Workday, navigate to your Worker Profile, click the Career link in the left hand navigation menu and then update the sections in your Professional Profile before you apply. This information will be pulled in to your application. The application is one page and you will be prompted to upload your resume. In addition, you must respond to the application questions presented to upload any additional Required Materials (letter of interest, references, etc.) that were noted above. ---- Employment Eligibility: Regular staff who have been employed in their current position for the last six continuous months are eligible for openings being recruited for through University-Wide or Open Recruiting, to include both promotional opportunities and lateral transfers. Staff who are promotion/transfer eligible may apply for positions without supervisor approval. ---- Retirement Plan Eligibility: The retirement plan for this position is Teacher Retirement System of Texas (TRS), subject to the position being at least 20 hours per week and at least 135 days in length. This position has the option to elect the Optional Retirement Program (ORP) instead of TRS, subject to the position being 40 hours per week and at least 135 days in length. ---- Background Checks: A criminal history background check will be required for finalist(s) under consideration for this position. ---- Equal Opportunity Employer: The University of Texas at Austin, as an equal opportunity/affirmative action employer, complies with all applicable federal and state laws regarding nondiscrimination and affirmative action. The University is committed to a policy of equal opportunity for all persons and does not discriminate on the basis of race, color, national origin, age, marital status, sex, sexual orientation, gender identity, gender expression, disability, religion, or veteran status in employment, educational programs and activities, and admissions. ---- Pay Transparency: The University of Texas at Austin will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor’s legal duty to furnish information. ---- Employment Eligibility Verification: If hired, you will be required to complete the federal Employment Eligibility Verification I-9 form. You will be required to present acceptable and original documents to prove your identity and authorization to work in the United States. Documents need to be presented no later than the third day of employment. Failure to do so will result in loss of employment at the university. ---- E-Verify: The University of Texas at Austin use E-Verify to check the work authorization of all new hires effective May 2015. The university’s company ID number for purposes of E-Verify is 854197. For more information about E-Verify, please see the following: E-Verify Poster (English and Spanish) [PDF] Right to Work Poster (English) [PDF] Right to Work Poster (Spanish) [PDF] ---- Compliance: Employees may be required to report violations of law under Title IX and the Jeanne Clery Disclosure of Campus Security Policy and Crime Statistics Act (Clery Act). If this position is identified a Campus Security Authority (Clery Act), you will be notified and provided resources for reporting. Responsible employees under Title IX are defined and outlined in HOP-3031. The Clery Act requires all prospective employees be notified of the availability of the Annual Security and Fire Safety report. You may access the most recent report here or obtain a copy at University Compliance Services, 1616 Guadalupe Street, UTA 2.206, Austin, Texas 78701.