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Advanced control of large-scale power electronics systems

Advanced control of large-scale power electronics systems for next-generation renewable energy integration. more...

Supervisor(s): Li, Sinan (Dr)

Geometric analysis of transcendental solutions of nonlinear systems

Modern science derives its power from mathematics. The project aims to capture, identify and describe pivotal, transcendental solutions of nonlinear systems that are universal in sc more...

Supervisor(s): Joshi, Nalini (Professor), Radnovic, Milena (Dr)

Real-time Low-power Neural Accelerator

This project aims at developing an Application-Specific Integrated Circuit (ASIC) for a low-power learning system processing real-time time-series data. more...

Supervisor(s): Kavehei, Omid (Dr)

Low-power Intelligent Bio-Signal Processing

This project aims at developing a responsive implant that is making decisions based on smart bio-signal processing. more...

Supervisor(s): Kavehei, Omid (Dr)

Non-orthogonal multiple access for massive Internet of Things

Non-orthogonal multiple access (NOMA) has been identified as a key technology in the fifth generation of mobile wireless standards to improve the network capacity. This project aims more...

Supervisor(s): Shirvanimoghaddam, Mahyar (Dr), Vucetic, Branka (Professor)

Design of Novel Channel Coding Techniques for Short Packet Transmission in Massive Internet of Things

In this project, the channel code design problem for massive Internet of Things (IoT) is considered by taking into account the unique characteristic of massive IoT, i.e., short leng more...

Supervisor(s): Shirvanimoghaddam, Mahyar (Dr), Li, Yonghui (Professor), Vucetic, Branka (Professor)

Data-intensive solutions for medical technologies

This project aims at developing advanced solutions in software for processing medical data, addressing the issue of less or no labelled data. more...

Supervisor(s): Kavehei, Omid (Dr)

Tracking antibiotic resistance genes and mobile genetic elements in the environment

Antibiotic resistance is a massive problem for medicine, and thus much effort is being put into understanding the genetic basis of how bacteria evolve so quickly to become resistant more...

Supervisor(s): Coleman, Nicholas (Associate Professor)

Machine learning in cell identity, fate decision, and lineage prediction

Multi-omics and single-cell omics approaches are transforming our understanding of stem cell-based regeneration of tissues and organs on the individual cell level. By generating and more...

Supervisor(s): Yang, Pengyi (Dr)

Fighting the spread of misinformation

The development of a quantitative measure of the spread of misinformation, for the purposes of developing a strategy to counter science denial. more...

Supervisor(s): Alexander , Tristram (Dr), Fulcher, Ben (Dr), Sharma, Manjula (Professor)