Computational Modelling and Control of Nonlinear and Fluidic Systems with Application to Immunology

Our laboratory specializes in the use of optimization-based methods to model, analyze and control uncertain, nonlinear and fluidic (PDE) systems. At the moment, we have need of a mathematically and computationally oriented PhD student to assist with data-based modelling the dynamics of immune response in mice as part of a collaboration with the department of bioengineering.

Suitable Background: Courses in any of the following: Optimization, Control Systems, Machine Learning, Dynamical Systems, Biological Modelling, Mathematical Analysis, Algorithms. Degree in Engineering, Mathematics, or Computer Science.

A Fall 2023 start is anticipated, although applications will accepted until the position is filled.  

Our lab has a broad mandate to perform advanced, computation-based research in dynamics, control theory, optimization, and machine learning. Significant accomplishments of our lab are the development of the SOSTOOLS and PIETOOLS software suites for optimization of polynomials and optimal control of PDEs. Application-based research includes robotics, control of nuclear fusion, information networks and immunology. See the website http://control.asu.edu for a list of recent research accomplishments.

The ideal candidate has an aptitude for mathematics and is interested in solving fundamental and high-impact, difficult problems in  several of the fields mentioned above. The work is challenging and selection is rigorous, so ideal candidates should have first class honors or equivalent from a top 100 university as per AWRU, USNWR or other major ranking system (2.1 OK for Oxford/Cambridge). For non-UK students, an undergraduate GPA of 3.6 (US), 90 (China), 18(Iran), 8(India), 9.0 (Mexico) is desired. In addition, an interview will be conducted to screen for academic aptitude.