40 postdoctoral-position-in-molecular-dynamic-simulation-self-assemble-polymer PhD positions at Cranfield University
Sort by
Refine Your Search
-
This is a fully funded PhD (fees and bursary) in experimental icing research. Fundamental understanding of droplet impact dynamics is integral to icing. The overall aim of this PhD is to use optical
-
explore the nonlinear structural dynamics of LGSs to fully understand the complexity of their control. They will use this foundation to explore idealised and realistic control laws to virtually “stiffen
-
Multiple self-funded PhD positions are available in Modelling and Simulation (M&S). The project will aim to mature software repositories describing the biomechanics of the human brain. The M&S tools
-
complex in scenarios like parallel-pass deposition, thin-wall deposition, and off-centre or out-of-position deposition. Additionally, FEA models are focused on thermal conduction in solid medium and often
-
This is a self-funded opportunity relying on Computational Fluid Dynamics (CFD) and wind tunnel testing to further the design of porous airfoils with superior aerodynamic efficiency. Building
-
FPGA/ASIC architectures that dynamically reconfigure based on AI workloads, optimizing performance, energy efficiency, and functionality. 3- Intelligent Interface Design: Create smart interfaces
-
Cranfield University and Magdrive, offer a fully funded PhD position under the umbrella of the R2T2 consortium to study the optimisation of their thruster for a kick stage. R2T2 is a UKSA-funded
-
-on experience with real-world SCADA data, industry collaboration with RES Group, and training in high-fidelity simulation environments (OpenFAST, Digital Twin technology). This opportunity is ideal for those
-
and controlling defects and lay the foundation for a thermal physics-based approach to process qualification. Additive manufacturing (AM) is a rapidly evolving technology that continues to drive
-
This research opportunity invites self-funded PhD candidates to develop advanced deblurring techniques for retinal images using deep learning and variational methods. Retinal images often suffer