RF​/Wireless Network Research Associate

Job Title

RF/Wireless Network Research Associate

Applied Research Laboratories

PICKLE RESEARCH CAMPUS

The Signal and Information Sciences Lab (SISL) is seeking a motivated individual to support and potentially lead research and development tasks relating to data science, machine learning, and algorithmic development related to RF networks. This may include tasks such as predicting propagation loss, testing new algorithms for link prediction, running network simulators, and predicting movement of nodes participating in a network.

Applicant must have a dynamic skill set, willing to work with new technologies and signal processing and propagation modeling techniques, 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 all groups of clients.

Able to work under pressure and accept supervision. Regular and punctual attendance.

US citizen required. 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.

• Designing, researching, and testing state-of-the-art algorithms applicable to a range of projects.
• Documenting research in form of publications and reports.
• Develop and maintain sponsor relationships and programs.
• Develop and maintain strategic partnerships with collaborators.
• Other related functions as assigned.

• PhD degree in Engineering, Computer Science, or other related discipline and demonstrated publication experience in relevant area.
• 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 in a research setting.
• Proficiency using Python for algorithm development, data science, and/or machine learning.
• Demonstrated ability to work effectively, both independently and collaboratively, with a strong commitment to contribution and enabling the entire team.

• Five years of related experience.
• Experience working in a mixed team of researchers and developers to implement and deploy novel research in usable software.
• Multiple publications related to RF networks.
• Demonstrated ability to implement and test data science and machine learning based techniques on a variety of problem domains, including applications outside of RF networks.
• Demonstrated ability to implement and test algorithms in a research environment.
• Cumulative GPA of 3.0.

$120,000–$165,000+/negotiable depending on qualifications.

• 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.

• 100% employer-paid basic medical coverage.
• Retirement contributions.
• Paid vacation and sick time.
• Paid holidays.

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

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.

The University of Texas at Austin will not discharge or in any other manner discriminate against employees or applicants because…