Openings

PhD programs through Aerospace Engineering/Mechanical Engineering at UTK

We are seeking highly motivated students to join our group. The ideal candidates will have a strong background in continuum mechanics, fluid dynamics, mathematics, computational methods, machine learning and data driven techniques. The research will involve the development of new algorithms, computational frameworks and numerical schemes, as well as the implementation of these methods on both canonical and real-world forward and inverse problems. The candidates will have the opportunity to work with cutting-edge technologies and high-performance computing platforms. They will also benefit from our participation in multi-institutional and international collaborative projects, fostering a dynamic and globally connected research environment. Possible topics are highlighted below. 

Several graduate research and/or teaching assistantships (RA/TA) are available for PhD students at the University of Tennessee Knoxville. If you are interested in joining us on Rocky Top, please get in touch with me for further information. 

P.S. The application portal offers a variety of concentration areas such as applied mechanics, thermal fluid mechanics, energy, nuclear science and systems controls, allowing you to choose your preferred focus in Knoxville. Within my lab, there are multiple RA and TA positions waiting to be filled. Those who are accepted into the aerospace engineering (AE) or mechanical engineering (ME) programs will have the opportunity to be considered for these positions, which come with a monthly stipend and coverage of tuition expenses. 

Presently, I'm exclusively considering PhD applicants. If you lack an MS degree, you're still eligible to apply for our PhD program. As you make progress towards your PhD, you'll also earn an MS degree certificate once you've accrued sufficient credits. Our program offers two pathways: direct PhD (starting after a BS degree) and regular PhD (requiring an MS degree). Direct PhD students must complete 42 course credits (equivalent to 14 courses) to fulfill degree requirements. If you hold an MS in AE or ME, you can transfer 24 credits from your previous degree, meaning you'll need to complete 6 more courses. Please note the Graduate Admissions application deadlines.

In your cover letter or statement of purpose, kindly emphasize your interest in one or more of the specified research topics. 

MABE graduate programs: https://mabe.utk.edu/academics/graduate-programs/

Start your application: https://gradschool.utk.edu/future-students/ 

Omer San

Development of scalable dynamic data assimilation methods for multiscale systems

The successful candidate will have a strong background in mathematics, computational methods, and data assimilation techniques. The research will involve the development of new algorithms and numerical schemes, as well as the implementation of these methods on high-performance computing platforms. The successful candidate will have the opportunity to work with cutting-edge technologies and high-performance computing platforms.

Development of hybrid analysis and modeling approaches for inverse problems

The ideal candidate will have a strong background in applied mathematics, numerical methods, and machine learning. The research will involve the development of new algorithms and numerical schemes that leverage the strengths of both physics-based models and data-driven approaches, as well as their implementation and validation on real-world inverse problems. The project will require working closely with domain experts to identify relevant applications and to validate the results.

Development of reinforcement learning approaches for closure modeling

The ideal candidate will have a strong background in machine learning, optimization, and numerical methods. The research will involve the development of new algorithms for learning closure models, as well as their implementation and evaluation on real-world systems. 

Development of communication cost reduction algorithms in distributed systems

The successful candidate will have a strong background in parallel computing, numerical methods, and optimization. The research will involve the development of new communication-efficient algorithms for distributed optimization, as well as the implementation of these methods on high-performance computing platforms. The project will provide access to cutting-edge computational resources and require working closely with domain experts to identify relevant applications and to validate the results.

Development of mathematical foundations for digital twins of complex processes

The ideal candidate will have a strong background in applied mathematics, numerical methods, and modeling. The research will involve the development of new mathematical models and algorithms for digital twins, as well as their implementation and validation on real-world systems. The project will require working closely with domain experts to identify relevant applications and to validate the results.

Development of quantum computing algorithms for multiscale systems

The successful candidate will have a strong background in quantum computing, mathematics, and computational methods. The research will involve the development of new algorithms for solving multiscale problems using quantum computing, as well as the implementation of these methods on quantum computing platforms. The project will require working closely with domain experts to identify relevant applications and to validate the results.

Development of model discovery and system identification approaches for turbulent flows

The successful candidate will have a strong background in fluid mechanics, applied mathematics, and machine learning. The research will involve the development of new algorithms and methodologies for discovering low-dimensional models and identifying dynamical systems underlying complex turbulent flows. 

Development of machine intelligent domain decomposition approaches for elliptic problems

The successful candidate will have a strong background in applied mathematics, computational methods, and machine learning. The research will involve the development of new algorithms and methodologies for domain decomposition that leverage machine learning techniques to accelerate the convergence of iterative solvers for elliptic problems. 

Development of probabilistic modeling frameworks in digital twins

The successful candidate will have a strong background in machine learning, statistical mechanics, applied mathematics, and computational physics. The research will involve the development of new algorithms and methodologies for probabilistic modeling of digital twin frameworks, as well as their implementation and validation on real-world problems. The candidate will have access to cutting-edge computational resources and will work in a collaborative environment with a team of experts in computing, cybernetics, statistical mechanics and computational physics.

Development of generative artificial intelligence frameworks in high-performance computing applications

The successful candidate will have a strong background in machine learning, high-performance computing, and optimization. The research will involve the development of new generative models and algorithms for high-performance computing applications, as well as their implementation and validation on real-world problems. The project will require working closely with domain experts to identify relevant applications and to validate the results. 

Development of uncertainty quantification approaches under mixed precision environments

The successful candidate will have a strong background in applied mathematics, computational methods, and high-performance computing. The research will involve the development of new algorithms and methodologies for quantifying uncertainties in large-scale scientific simulations, leveraging mixed precision computing to enable higher accuracy and efficiency.