A fully supported PhD position on Machine Learning for Scientific Computing is available in the 
Department of 
Mechanical and Aerospace Engineering at Rutgers University. The successful applicant will work under the 
supervision of Dr. George Moutsanidis and the desired start date is January 2026 or September 2026.

The PhD project will focus on the development and application of advanced machine learning techniques 
on 
computational fluid dynamics and computational solid mechanics. This includes both data-driven and 
physics-informed approaches, with potential applications in optimization, surrogate modeling, and 
uncertainty quantification.

Required Qualifications:
•	MS degree in Engineering, Applied Mathematics, or related field
•	Solid academic background in the theory and application of novel machine learning techniques, such 
as, 
deep operator networks, graph neural networks, and generative AI methods.
•	Solid academic background on scientific computing
•	Excellent coding skills
•	Exceptional analytical and problem-solving skills
•	Strong communication and teamwork capabilities

Why join Rutgers University? Rutgers University-New Brunswick is the flagship campus of New Jersey’s 
state 
university system and a member of the Big Ten Academic Alliance. Located just 40 minutes from New York 
City and within easy reach of Philadelphia, it offers access to a vibrant academic and industrial ecosystem. 
The Department of Mechanical and Aerospace Engineering is home to a dynamic group of faculty with 
diverse 
expertise, strong external funding, and state-of-the-art research facilities. Rutgers is also a designated R1 
research university, providing a stimulating environment for interdisciplinary research and collaboration 
across engineering, computer science, and applied mathematics.

The interested applicants are encouraged to communicate their interest to Dr. Moutsanidis by sending an 
email (george.moutsanidis@rutgers.edu) and by submitting the following material:
•	Graduate and undergraduate transcripts
•	CV
•	Any relevant publications
•	Contact information of at least 3 faculty members that can provide letters of recommendation
•	Results of GRE and TOEFL if available

The review of applications will begin immediately, and the position will remain open until filled.