PhD Graduate - Postdoctoral Researcher - In-Situ Sensing Additive Manufacturing

Overview

Are you passionate about pioneering advancements in additive manufacturing through cutting-edge sensing technologies, data fusion, and real-time control?

Do you want to contribute to critical national challenges by enabling intelligent closed-loop monitoring and control of metal additive manufacturing processes? Join our innovative research team in the Research and Exploratory Development Department (REDD) at the Johns Hopkins University Applied Physics Laboratory (JHU/APL). As an In-Situ Sensing Postdoctoral Fellow, you will develop and integrate novel sensing modalities, artificial intelligence (AI), and machine learning (ML) algorithms to enhance process control, optimize material properties, and ensure the reliability of additively manufactured components.

Your work will be essential in designing closed-loop control systems that adapt dynamically to real-time process data, enabling unprecedented advancements in manufacturing precision and efficiency.

Our team is actively developing next-generation sensing and control solutions that will allow real-time adjustments to critical additive manufacturing parameters, such as laser power, scan speed, and material feed rate. By leveraging multi-modal sensor data—including optical, thermal, acoustic, and X-ray imaging—you will help to create intelligent feedback systems capable of identifying defects, predicting failure points, and optimizing manufacturing conditions. These advances will not only push the limits of metal additive manufacturing but will also enable new applications in mission-critical environments where reliability is paramount.

As an In-Situ Sensing Postdoctoral Fellow, you will…

• Collaborate with APL scientists, engineers, and technicians to develop novel closed-loop sensing and control solutions tailored for additive manufacturing.
• Perform pioneering research in materials and process characterization by fusing in-situ sensing modalities to optimize microstructure and density in metal additive manufacturing.
• Utilize AI and ML algorithms to extract insights from multi-modal sensor data, improve real-time process monitoring, and drive automated control systems.
• Design, implement, and validate adaptive control algorithms that leverage sensor feedback to dynamically adjust processing parameters in real time.
• Engage with a multidisciplinary team focused on materials discovery, novel fabrication techniques, multiscale modeling, processing insights, advanced testing, and qualification science.
• Present technical findings to both internal and external audiences, effectively communicating complex concepts to team members, task leads, and project leadership.
• Contribute to the design, fabrication, and characterization of additively manufactured operational prototypes that demonstrate intelligent process control.

• Ph.D. in Mechanical Engineering, Electrical Engineering, Materials Science, Data Science, or a related field.
• Strong written and oral communication skills, with the ability to engage broad audiences and adapt to different communication styles.
• Adaptable, enthusiastic about new challenges, and collaborative with a mindset open to feedback.
• Experience solving multidisciplinary research challenges related to qualified hardware and additive manufacturing.
• Fundamental understanding of additive manufacturing, including process defects, microstructure evolution, and thermodynamic solidification.
• Demonstrated track record of authoring research proposals and publishing high-impact journal papers.
• Willing and able to work in a laboratory setting and travel for field testing, sponsor meetings, conferences, and technical presentations.
• Ability to obtain a Secret-level security clearance to start with APL; final Top Secret clearance may be required. If selected, you will be subject to a government security clearance investigation and must meet eligibility requirements for access to classified information, including U.S. citizenship.

• Experience fielding additively manufactured components.
• Ability to fuse 2D and 3D data, including in-situ and post-manufacturing data, into a 3D format for…