Advanced Manufacturing Research Assistant - Department of Engineering Technologies

The student will participate in a NSF project which will adopt a data-centric AI approach to identifying the most influential process parameters governing mechanical properties in advanced manufacturing. Student will explore feature dominance, redundancy, and interaction effects using interpretable machine learning techniques. The study emphasizes how data quality, feature selection, and preprocessing impact model performance. Results will support more efficient experimental design and smarter manufacturing control.

• Identify when additional information is needed before making a decision.
• Break down tasks into steps to complete work more efficiently.
• Use campus tools and systems effectively to complete job tasks.
• Stay aware of basic cybersecurity and data privacy practices.
• Troubleshoot simple technical issues or know when to ask for help.

• Data Collection:
  Assist in collecting and organizing data through experiments.
• Literature Reviews:
  Conduct comprehensive literature reviews to gather relevant research findings and inform project direction.
• Data Analysis:
  Assist in analyzing collected data using statistical software or qualitative analysis techniques.
• Report Writing:
  Contribute to writing project reports, research papers, and presentations based on findings and analysis.
• Collaboration:
  
  Work closely with faculty members, fellow students, and project stakeholders to achieve project goals.

• Currently enrolled in an undergraduate program in any applied sciences, statistics, and engineering.
• Strong organizational and time-management skills.
• Excellent communication skills, both written and verbal.
• Ability to work independently and collaboratively in a team environment.
• Organized and able to remain productive.
• Ability to write technical reports.
• Proficiency in Microsoft Office suite and familiarity with statistical analysis software (e.g., SPSS, R) preferred.
• Prior experience in research or data collection is advantageous but not required.