-
the Intelligent Systems Research group (with extensive experience in deploying machine learning models for edge devices and microcontrollers and building simulation environments for IoT), and our extensive network
-
, this PhD project aims to: (i) develop physics-informed machine learning (PIML) approaches to construct surrogate models of deep geothermal systems that accurately reproduce the behaviour of high-fidelity
-
and architectures that support efficient, secure, and scalable machine learning operations (MLOps) across resource-constrained environments for Edge AI. Ethical, and responsible FL for healthcare: In
-
equations to simulate pollutant transport, mixing and biochemical processes. To enable rapid prediction, a machine-learning surrogate model based on Gaussian process regression will be developed and trained
-
: · Learning how to express software requirements precisely using formal models. · Using these specifications to automatically generate test cases for software systems and code. · Exploring how test
-
machine learning and AI research. Strong analytical thinking, problem-solving skills, and the ability to engage with complex data challenges will be greatly valued. Experience with Python or AI frameworks