Electrical & Computer Engineering Researcher – GNSS & Software

University of Texas; United States; On-site; USD 120,000 - 165,000; Full time

Benefits offered by this job

100% employer-paid basic medical

Retirement contributions

Paid vacation and sick time

Paid holidays

Job summary

The University of Texas at Austin's Applied Research Laboratories seeks a Research Associate with expertise in electrical and/or computer engineering. The role focuses on computational methods, GNSS, and remote sensing, with opportunities to publish and present findings.

The ideal candidate will have a PhD and strong programming experience in C++ or Python, plus a track record of applying statistical and computational algorithms to scientific problems.

Qualifications

• PhD in Electrical Engineering, Computer Engineering, Computational Science, Computational Engineering, Aerospace Engineering, Computer Science, Math, Physics, or related discipline.
• Experience developing software in C++ or Python for scientific applications.
• One or more years post-graduate experience developing statistical and computational 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 electrical and/or computer engineering research relevant to the interests of SGL.
• Present and publish your research, both internally and externally.
• Contribute to problem formulations, algorithm development, systems designs, software implementations, and data collection methods.

Skills

C++

Python

Software development

Publications

Education

PhD in Electrical Engineering or related

Tools

Verilog

VHDL

Linux

Job description

The University of Texas at Austin's Applied Research Laboratories seeks a Research Associate with expertise in electrical and/or computer engineering. The role focuses on computational methods, GNSS, and remote sensing, with opportunities to publish and present findings.

The ideal candidate will have a PhD and strong programming experience in C++ or Python, plus a track record of applying statistical and computational algorithms to scientific problems.