PhD Position in Physics-Guided Machine Learning for Membrane Formation via NIPS

This PhD position at Middle East Technical University (METU) invites a highly motivated candidate to join a research group specializing in membrane and membrane-based processes. The project focuses on developing physics-guided and physics-informed machine learning approaches to model the nonsolvent-induced phase separation (NIPS) process, critical for ultrafiltration membrane formation. The successful applicant will contribute to advancing membrane technology for applications in separations, artificial organs, and microfluidic systems. The work involves computational modeling, machine learning, and experimental validation, offering a unique interdisciplinary opportunity at the intersection of chemical engineering and data science.

• Develop physics-guided and physics-informed machine learning models to simulate the NIPS process for ultrafiltration membrane formation.
• Conduct computational experiments to analyze membrane formation dynamics and mass transport phenomena.
• Validate models using experimental data and contribute to experimental design where applicable.
• Collaborate with the research group to advance membrane technology for applications in separations, artificial organs, and microfluidic systems.
• Present research findings at conferences and publish results in high-impact journals.
• Participate in group meetings and contribute to the broader research agenda of the Chemical Engineering department.