PhD student

Position overview

The PhD student will develop an original contribution in control, optimization, cyber-physical systems, or sustainable energy. The work will progress from a focused research question through modelling, method development, validation, publication, and thesis completion.

The final topic and plan will be agreed with the supervisor and must satisfy Khalifa University’s doctoral requirements.

Research topics

• Model predictive control (MPC), robust, stochastic, distributed, adaptive, and learning-enabled control.
• Nonlinear and hybrid systems, stability analysis, safety, and formal methods.
• System identification, state estimation, scientific machine learning, and digital twins.
• Renewable microgrids, converter control, batteries, and resilient energy systems.
• Green hydrogen, electric mobility, embedded control, and industrial automation.

Doctoral responsibilities

• Define the literature gap, research questions, methods, milestones, and validation plan with the supervisor.
• Develop an original analytical, computational, or experimental contribution and maintain its research software.
• Compare the proposed method with credible baselines using simulation, data, HIL, or experiments as appropriate.
• Communicate progress clearly, respond to evidence and peer review, and follow research-integrity requirements.
• Prepare journal papers, conference presentations, and a coherent doctoral dissertation.

Required qualifications

• Eligibility for an approved Khalifa University doctoral-admission route.
• An MSc in a relevant discipline, or an approved direct-entry qualification, as specified by the university.
• Strong foundations in mathematics, dynamical systems, control, optimization, and scientific programming.
• Evidence of research potential through a thesis, project, publication, report, or software repository.
• Good written and spoken English, intellectual independence, and the ability to collaborate.

Preferred experience

• A thesis or project in control, optimization, cyber-physical systems, or energy engineering.
• MATLAB/Simulink, Python, C/C++, numerical optimization, and Git-based workflows.
• System identification, state estimation, MPC, scientific machine learning, or digital twins.
• Renewable energy, power electronics, green hydrogen, electric mobility, or embedded systems.
• Real-time simulation, hardware-in-the-loop (HIL), power hardware-in-the-loop (PHIL), laboratory work, or analysis of measured data.