Scientific Machine Learning for Turbulent Reacting Flow Simulations

The Hasti Lab at the University of Central Florida is recruiting a fully funded PhD student for research in scientific machine learning applied to turbulent reacting flow simulations in energy systems. The position starts in Spring 2025 and offers a Graduate Research Assistantship with a competitive stipend, tuition waiver, and access to advanced computational resources including GPUs. The research will focus on developing energy-efficient, trustworthy, and adaptive machine learning algorithms for turbulent reacting flows, designing deep learning-based surrogate modeling frameworks, implementing data assimilation and multimodal learning techniques, and leveraging high-performance computing for complex energy systems. Applicants should have a Bachelor’s or Master’s degree in mechanical or aerospace engineering, computational science and engineering, applied mathematics, computational physics, or related fields. Required skills include data science, AI, machine learning, fluid dynamics, turbulence, combustion, numerical methods, and computational fluid dynamics. Preferred skills include experience with PINNs, DeepONets, Neural Operators, PyTorch or TensorFlow, GPU-based computing, strong programming in Python, CUDA, C, or C++, and excellent communication and writing abilities. The lab offers professional development opportunities and participation in impactful research advancing AI for science and engineering. To apply, send your CV, cover letter, and contact details for three references to Prof. Veeraraghava Raju Hasti at [email protected]. Applications are open until the position is filled.