Associate Researcher – Data Analysis

About the Role

The University of Dayton Research Institute's (UDRI) Advanced Manufacturing Technology Development (AMTD) group is seeking an engineer/scientist to drive and manage research in data analysis for metal additive manufacturing (AM). This work will advance novel research focused on sensor-based in-situ process monitoring, data correlation to end part quality, data-driven machine parameterization, and data management solutions.

Responsibilities

• Develop analysis tools to extract physical meaning from in-situ process data.
• Develop machine learning algorithms to drive correlation between in-situ process data and end part quality, particularly NDE.
• Perform data fusion, registration, and calibration of various data streams.
• Plan and execute design of experiments, particularly involving unique, cutting-edge LPBF strategies.
• Build and route specimens for data collection, characterization, testing, and correlation.
• Interface with data collection and management tools for large datasets.
• Integrate and modify in-situ process sensors.
• Operate in-situ sensors/suites for laser powder bed fusion machinery.
• Develop new sensing modalities for understanding the laser powder bed fusion process.

Requirements

• A bachelor's degree in engineering or computer science from an accredited University.
• Coding/programming skills (Python preferred).
• Effective ability to communicate both written and verbally, work well with others, and think critically to drive a project to completion with little supervision.
• U.S. citizenship (due to requirements of research contracts with the U.S. federal government).

Preferred Qualifications

• Hands-on experience in the lab with custom and commercial LPBF systems and all supporting equipment.
• Experience with machine learning concepts.
• Experience with image processing techniques.
• Experience with DREAM.3D software.
• Experience performing design of experiments (DOEs) and statistical analysis.
• Demonstrated experience with laser-based manufacturing equipment in support of research.
• Understanding of lasers and optics.
• Demonstrated experience with sensors for dynamic processes.
• Understanding of laser/material interaction.
• Understanding of AM process modeling tools.
• Demonstrated experience with data acquisition and analysis concepts.