Employment Opportunities

Associate Additive Manufacturing Researcher, Data Analysis

Apply now Job No: 501953
Work Type: Staff Full Time (1500 hours or greater)
Location: Dayton, OH
Category: Research Staff
Department: Add Mfg Tech Development - 250465
Pay Grade: R1 - Exempt
Advertised:
Applications close:

Position Summary:

The University of Dayton Research Institute's (UDRI) Additive Manufacturing Technology Development (AMTD) group is seeking an engineer/scientist to drive and manage research in data analysis for metal additive manufacturing (AM). UDRI is a national leader in scientific and engineering research, serving government, industry and nonprofit customers. This is a full time benefit eligible position that offers health, dental and vision insurance, retirement, disability, vacation and sick accrual and paid Holidays. Join the UDRI team and apply now!

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. This position will perform research designed to advance current understanding of the effects of laser manufacturing processes on materials through sensor development, implementation, data collection, and data analysis. Work activities may include the following.
• Developing analysis tools to extract physical meaning from in-situ process data.
• Developing machine learning algorithms to drive correlation between in-situ process data and end part quality, particularly NDE.
• Data fusion, registration, and calibration of various data streams.
• Planning and executing design of experiments, particularly involving unique, cutting edge, LPBF strategies.
• Building and routing of specimens for data collection, characterization, testing, and correlation.
• Interfacing with data collection and management tools for large datasets.
• Integrating and modifying in-situ process sensors.
• Operating, in-situ sensors/suites for laser powder bed fusion machinery.
• Developing new sensing modalities for understanding of the laser powder bed fusion process.

Minimum Qualifications:

• A bachelor's degree in engineering or computer science from an accredited University.
• Coding/programming skills.
• Effective written and verbal communication skills.
• Ability to work well with others.
• Strong critical thinking skills.
• Ability to drive a project to completion with little supervision.
• Due to the requirements of our research contracts with the U.S. federal government, candidates for this position must be a U.S. citizen.

Preferred Qualifications:

While not everyone may possess all of the preferred qualifications, the ideal candidate will bring many of the following:

• Hands on experience in the lab with custom and commercial LPBF systems and all supporting equipment.
• Python coding experience is preferred.
• Experience with machine learning concepts.
• Experience with image processing techniques.
• Experience with DREAM.3D software.
• Experience performing design of experiments (DOE's) 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.

Special Instructions to Applicants: To apply please submit a cover letter addressing each minimum qualification and any applicable preferred qualifications that you meet.
Closing Statement:

Informed by its Catholic and Marianist mission, the University is committed to the principles of diversity, equity, and inclusion. Informed by this commitment, we seek to increase diversity, achieve equitable outcomes, and model inclusion across our campus community. As an Affirmative Action and Equal Opportunity Employer, we will not discriminate against minorities, women, protected veterans, individuals with disabilities, or on the basis of age, race, color, national origin, religion, sex, sexual orientation or gender identity.

 

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