| Full Description |
Postdoctoral Researcher Position - Additive Manufacturing, Process Control, and Digital Twins
We are seeking a highly motivated Postdoctoral Researcher to join an interdisciplinary team working at the forefront of advanced manufacturing. This position focuses on integrating additive manufacturing, in-situ sensing, data fusion, and digital twin frameworks to enable real-time process control and to establish robust correlations between manufacturing conditions and mechanical properties.
The successful candidate will contribute to the development of intelligent manufacturing systems that leverage multi-modal sensor data and image-based analytics to improve process reliability, part quality, and performance prediction in additively manufactured components.
Key Responsibilities:
- Develop and optimize additive manufacturing processes (laser powder bed fusion or extrusion-based systems).
- Design and implement in-situ monitoring strategies using multi-modal sensors (e.g., physical, thermal, optical, acoustic).
- Apply data fusion techniques to integrate heterogeneous sensor streams for real-time process understanding.
- Develop digital twin frameworks to model, simulate, and predict process behavior and material outcomes.
- Utilize image processing and computer vision techniques to analyze melt pool dynamics, layer quality, and defect formation.
- Correlate process parameters and sensor data with resulting mechanical properties (e.g., strength, fatigue, microstructure).
- Conduct mechanical testing and materials characterization to validate predictive models.
- Collaborate with cross-functional teams in materials science, mechanical engineering, and data science.
- Publish research findings in high-impact journals and present at conferences.
Required Skills and Qualifications:
- Ph.D. in Electrical Engineering, Mechanical Engineering, Materials Science, Manufacturing Engineering, or a related field.
- Strong background in additive manufacturing processes and materials.
- Experience with process monitoring, sensing technologies, and data acquisition systems.
- Proficiency in data analysis, machine learning, or data fusion techniques.
- Experience with image processing and computer vision (e.g., Python, MATLAB, OpenCV).
- Familiarity with modeling and simulation approaches relevant to digital twins.
- Knowledge of mechanical testing and structure-property relationships.
- Excellent written and verbal communication skills.
- Demonstrated publication record in peer-reviewed journals.
- Ability to work independently and in a collaborative, interdisciplinary environment.
Preferred Qualifications:
- Experience with real-time process control systems.
- Knowledge of metallurgy and microstructure evolution in additively manufactured materials.
- Familiarity with high-performance computing or cloud-based simulation environments.
- Experience translating research into industrial or applied manufacturing settings.
What We Offer:
- Opportunity to contribute to next-generation smart manufacturing technologies.
- Access to advanced additive manufacturing and characterization facilities.
- A collaborative and innovative research environment.
- Competitive salary and benefits commensurate with experience.
|