The University of Texas at Austin
RF/Wireless Network Engineering Scientist
The University of Texas at Austin, Austin, Texas, us, 78716
Overview
The Signal and Information Sciences Laboratory (SISL) is seeking a motivated individual to support research and development tasks relating to software development, algorithmic development and data analytics for RF networks. This may include tasks such as predicting propagation loss, testing new algorithms for link prediction, and running network simulators. Responsibilities
Designing software applications and algorithms to support RF network projects and applications. Writing flexible and maintainable software code according to software best practices. Researching and testing state-of-the-art algorithms applicable to a range of projects. Deploying and supporting software delivered outside of ARL:UT. Other related functions as assigned. Required Qualifications
Master\'s degree in Engineering, Computer Science, or other related discipline and at least three years of experience working in a related field. Experience in multiple of the following: RF networks, RF network data, network simulators, wireless networks Demonstrated ability to implement and test machine learning based techniques in relevant or related problem domains. Demonstrated ability to work with subject matter experts when developing analytics. Proficiency using Python for algorithm development, data science, and/or machine learning. Familiar with the software development lifecycle process to design, develop, and maintain software. Knowledgeable using tools to perform testing, version control, and documentation of code. Demonstrated ability to work effectively, both independently and collaboratively, with a strong commitment to contribution and enabling the entire team. Applicant must be highly organized, have excellent attention to detail, and possess sound scientific judgment. The position will require effective management of their own time with the ability to plan, coordinate, and execute multiple tasks simultaneously. Regular and punctual attendance in the workplace is required. 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
Master\'s degree in Engineering, Computer Science, or other related discipline. Five years of related experience Demonstrated ability to implement and test data science and machine learning based techniques on a variety of problem domains, including network applications. Experience using C++ to develop and run software applications. Experience using JavaScript for front-end web development. Prior work experience in research environment. Experience using containerized applications in deployed environments. 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 (www.arlut.utexas.edu) for additional information about Applied Research Laboratories. Employment Information
UT Austin offers a competitive benefits package that includes: 100% employer-paid basic medical coverage Retirement contributions Paid vacation and sick time Paid holidays Salary Range: $104,000-$174,000+/negotiable depending on qualifications Working Conditions: Standard office conditions; Repetitive use of a keyboard at a workstation; Some weekend, evening and holiday work; Possible interstate/intrastate travel Required Materials: Resume/CV; 3 work references with contact information; Letter of interest; Unofficial college transcripts Equal Opportunity Employer: The University of Texas at Austin is an equal opportunity/affirmative action employer 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. Employment Eligibility: If hired, you will be required to complete the federal Employment Eligibility Verification I-9 form. You will need to present acceptable documents no later than the third day of employment. E-Verify: UT Austin uses E-Verify to check work authorization for all new hires. See the online E-Verify poster and Right to Work posters for details. Clery Act and Compliance: Employees may be required to report violations of law under Title IX and the Clery Act; the university may provide resources for reporting and compliance information is available through University Compliance Services. For more information, visit the UT Austin Careers pages.
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The Signal and Information Sciences Laboratory (SISL) is seeking a motivated individual to support research and development tasks relating to software development, algorithmic development and data analytics for RF networks. This may include tasks such as predicting propagation loss, testing new algorithms for link prediction, and running network simulators. Responsibilities
Designing software applications and algorithms to support RF network projects and applications. Writing flexible and maintainable software code according to software best practices. Researching and testing state-of-the-art algorithms applicable to a range of projects. Deploying and supporting software delivered outside of ARL:UT. Other related functions as assigned. Required Qualifications
Master\'s degree in Engineering, Computer Science, or other related discipline and at least three years of experience working in a related field. Experience in multiple of the following: RF networks, RF network data, network simulators, wireless networks Demonstrated ability to implement and test machine learning based techniques in relevant or related problem domains. Demonstrated ability to work with subject matter experts when developing analytics. Proficiency using Python for algorithm development, data science, and/or machine learning. Familiar with the software development lifecycle process to design, develop, and maintain software. Knowledgeable using tools to perform testing, version control, and documentation of code. Demonstrated ability to work effectively, both independently and collaboratively, with a strong commitment to contribution and enabling the entire team. Applicant must be highly organized, have excellent attention to detail, and possess sound scientific judgment. The position will require effective management of their own time with the ability to plan, coordinate, and execute multiple tasks simultaneously. Regular and punctual attendance in the workplace is required. 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
Master\'s degree in Engineering, Computer Science, or other related discipline. Five years of related experience Demonstrated ability to implement and test data science and machine learning based techniques on a variety of problem domains, including network applications. Experience using C++ to develop and run software applications. Experience using JavaScript for front-end web development. Prior work experience in research environment. Experience using containerized applications in deployed environments. 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 (www.arlut.utexas.edu) for additional information about Applied Research Laboratories. Employment Information
UT Austin offers a competitive benefits package that includes: 100% employer-paid basic medical coverage Retirement contributions Paid vacation and sick time Paid holidays Salary Range: $104,000-$174,000+/negotiable depending on qualifications Working Conditions: Standard office conditions; Repetitive use of a keyboard at a workstation; Some weekend, evening and holiday work; Possible interstate/intrastate travel Required Materials: Resume/CV; 3 work references with contact information; Letter of interest; Unofficial college transcripts Equal Opportunity Employer: The University of Texas at Austin is an equal opportunity/affirmative action employer 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. Employment Eligibility: If hired, you will be required to complete the federal Employment Eligibility Verification I-9 form. You will need to present acceptable documents no later than the third day of employment. E-Verify: UT Austin uses E-Verify to check work authorization for all new hires. See the online E-Verify poster and Right to Work posters for details. Clery Act and Compliance: Employees may be required to report violations of law under Title IX and the Clery Act; the university may provide resources for reporting and compliance information is available through University Compliance Services. For more information, visit the UT Austin Careers pages.
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