2026 PhD Graduate - Postdoctoral Researcher - In-Situ Sensing for Additive Manufacturing
Johns Hopkins APL · Laurel, MD · Manufacturing -Additive or Advanced
About this role
Johns Hopkins APL is hiring a junior-level Research Scientist in the machine learning function based in Laurel, MD. The posting calls out experience with Python, TensorFlow, PyTorch, scikit-learn. Listed education preference: a Ph.D. or equivalent.
- Role
- Research Scientist
- Function
- machine learning
- Level
- junior
- Track
- Individual contributor
- Location
- Laurel, MD
- Education
- Ph.D. preferred
- Department
- Manufacturing -Additive or Advanced
- Posted
- Sep 22, 2025
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Job description
from Johns Hopkins APL careersAre 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?
If so, we invite you to 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 be at the forefront of developing and integrating 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.