PhD student - algorithms for flow sensibilty analyisis using GPU supercomputers (R1)

Overview

Position: PhD student - algorithms for flow sensitivity analysis using GPU supercomputers (R1)

Reference: 295_26_CASE_LSCFD_R1

Starting date: 01/08/2026

Closing date: Wednesday, 15 July, 2026

Context and Mission

The quest for carbon-neutral aviation propels us toward uncharted technological frontiers. TRANSDIFFUSE is an ERC Synergy project to develop an AI-driven model called FluidGPT for clean propulsion and advanced flow control. The research blends numerical modelling, high-fidelity CFD, and experimental work across collaborating institutions.

Key Duties

• Develop novel algorithms for flow sensitivity using large data sets accelerated by GPUs.
• Assess the developed algorithms in TRANSDIFFUSE basic test cases.
• Participate in the CFD activities of the LS/CFD team in CASE.
• Develop scalable algebraic libraries and frameworks for GPUs.