Multi-Source Data Fusion & Digital Twin Research Associate

utaustin; United States; On-site; USD 90,000 - 120,000; Full time

Job summary

Applied Research Laboratories at The University of Texas at Austin seeks a Multi-Source Data Fusion Research Associate on the Pickle Research Campus to advance multi-source data fusion, environmental and geospatial modeling, and ML within SGL. You will design and evaluate fusion algorithms, develop digital twin and multi-agent simulations, and support decision-making with predictive analytics.

The role requires a PhD in a relevant field, demonstrated software experience for scientific

Qualifications

• PhD in engineering, computational science, physics, mathematics, geospatial or related field.
• 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.

Responsibilities

• Perform high-level independent research in multi-source data fusion, environmental and geospatial modeling, ML, and digital twin/MAS relevant to SGL.
• Present and publish research internally and externally; participate in reviews and interface meetings.
• Develop algorithms and designs for data fusion and modeling applications; employ statistical estimation and geospatial modeling.
• Develop multi-source data fusion algorithms to improve detection, correlation, and classification.
• Develop environmental suitability models to predict routes and movement patterns and simulate courses of action.
• Contribute to digital twin and multi-agent simulation to generate predictions and support decision making.
• Design experiments and analyses to gauge performance of fusion, classification, routing, and decision support methods.
• Implement software algorithms in a collaborative environment and document interfaces and tests.
• Advance knowledge of multi-source data fusion, estimation, ML, environment modeling, and software development principles.
• Provide guidance to staff and student employees and identify new research directions.
• Occasional travel to support meetings.

Skills

Data fusion

Machine learning

Geospatial modeling

Statistical estimation

Technical communication

Education

PhD in engineering or related field

Tools

Python

MATLAB

Job description

Applied Research Laboratories at The University of Texas at Austin seeks a Multi-Source Data Fusion Research Associate on the Pickle Research Campus to advance multi-source data fusion, environmental and geospatial modeling, and ML within SGL. You will design and evaluate fusion algorithms, develop digital twin and multi-agent simulations, and support decision-making with predictive analytics.

The role requires a PhD in a relevant field, demonstrated software experience for scientific