Data Fusion & Geospatial ML Research Associate

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

Benefits offered by this job

100% employer-paid basic medical cover

Retirement contributions

Paid vacation and sick time

Paid holidays

Job summary

The University of Texas at Austin’s Applied Research Laboratories seeks a Multi-Source Data Fusion Research Associate to advance research in data fusion, environmental modeling, and machine learning at the Pickle Research Campus. The role expects independent, publication-worthy work and collaboration across teams to push geospatial sensing and simulation capabilities forward.

Ideal candidates hold a PhD in a relevant field, have a strong software background, and can produce peer-reviewed outputs

Qualifications

• PhD in relevant engineering or science 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.
• Ability to work independently with sensitive information and manage multiple tasks.

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 SGL.
• Present and publish research, internally and externally, including project reviews and conferences.
• Develop algorithms and software for fusion, object classification, and tracking.
• Develop environmental suitability models to predict routes and movement patterns.
• Contribute to digital twin and multi-agent simulation capabilities for predictions and decision support.
• Design experiments and analyses to gauge performance of methodologies and provide recommendations.

Skills

Software development

Data fusion

Machine learning

Geospatial modeling

Statistical estimation

Communication

Team collaboration

Education

PhD in engineering or related field

Tools

Python

C/C++

FORTRAN

Linux

Windows

Git

GitHub/GitLab

AWS

LaTeX

Job description

The University of Texas at Austin’s Applied Research Laboratories seeks a Multi-Source Data Fusion Research Associate to advance research in data fusion, environmental modeling, and machine learning at the Pickle Research Campus. The role expects independent, publication-worthy work and collaboration across teams to push geospatial sensing and simulation capabilities forward.

Ideal candidates hold a PhD in a relevant field, have a strong software background, and can produce peer-reviewed outputs