Available Master topics: Scientific Computing
Unravel the effect of SK channel and its pharmacological block in health and atrial fibrillation, using computational models at different levels of the heart.
The goal is to understand, compare, and enhance classical approaches and approaches based on machine learning techniques to solve medical image registration problems.
In this project we will use numerical simulations (finite element method) to understand fluid patterns in and around the brain. You will work with detailed meshes of the human brain and investigate different theories on fluid flow and transport in the brain related to diseases such as Alzheimer's and Hydrocephalus.
The heart is the organ responsible for pumping blood around in your body. The heart consist of a tissue known as myocardium which is known to be a anisotropic, nonlinear, visco-elastic and nearly incompressible material. In order to create realistic models of the mechanics of the heart we would therefore need to incorporate these effects as well as appropriate boundary conditions.
In this master, you will add RDMA functionality to the global shared filesystem BeeGFS™️ (ThinkParQ) when running over PCIe NTB interconnects from Dolphin Interconnect Solution.
Cardiac mechanics is traditionally modeled using partial differential equations (PDEs) which are solved with the finite element method. In this traditional approach one selects a discrete mesh that represents the geometry of the
heart, and approximate the solution in the nodes of this mesh. Physics-informed neural networks (PINNs) is an alternative and relatively new method, which is based on deep learning and can be used as a surrogate to conventional finite element modeling. PINNs could be a promising technique to combine machine learning with traditional physics-based modeling.
The goal is to understand, compare, and enhance classical approaches and approaches based on machine learning techniques to solve topology optimization in fluid applications.