Postgraduate Studies in Computer & Electronic Engineering


The MUST research group offers a postgraduate programme focussed on machine learning. To qualify, prospective students must have an undergraduate degree in a related Engineering field and an interest in machine learning, deep neural networks, statistical pattern recognition and/or generalisation within the context of Artificial Intelligence.


MUST students either study the essence of the learning process of different types of deep networks, or apply and improve these techniques in the context of a specific application domain. In practice, students work with MuST researchers on one of our research projects, using popular deep learning tools (such as Pytorch and Tensorflow) to explore specific questions on new and existing data sets. Our students learn about machine learning algorithms, development of software, and the design and interpretation of machine learning experiments.


Through its affiliation with the South African Centre for Artificial Intelligence Research (CAIR), MUST is able to offer a number of M.Eng and PhD bursaries annually. We consider exceptional applications throughout the year (space allowing), and have an open application process in August-September.


MUST students enjoy considerable in-group support during their studies. Senior students are available to assist new students, resources are regularly shared on the group’s various communication channels, students meet with the entire group weekly and at least every second week with their supervisors in smaller groups. An official socials organiser sees to an appropriate portion of fun and downtime.

Besides learning how to conduct research, postgraduate study at MUST is also a “finishing school” of sorts for engineers. Our students work in an informal office setting where they learn to balance the freedom to explore with a variety of responsibilities, such as contributing to group activities, participating in collaborative software development, sharing research results and guiding younger researchers when the time comes. Students leave MUST having learnt essential soft skills that will help them hit the ground running at their next opportunity, with a healthy understanding of work-life balance.

Recent topics:

  • Deep learning theory
    • Generalisation in deep convolutional neural networks (PhD, current)
    • Exploring data utilisation in neural networks (MEng, current)
    • Interpreting deep neural networks with sample sets  (MEng, current
    • Generalisation in deep learning: Bilateral synergies in MLP learning (PhD, 2021)
    • Parametric studies of translation invariance and distortion robustness in Convolutional Neural Networks (MEng, 2020)
    • Contrasting Convolutional Neural Networks with alternative architectures for transformation invariance (MEng, 2020)
    • Activation functions in deep neural networks (MEng, 2019)
  • Space weather applications of DNNs
    • ​Deep neural networks for prediction of solar flares (MEng, 2020)
    • Interpretability of deep neural networks for SYM-H prediction (MEng, 2020)
    • Knowledge discovery with additive attribution methods for geomagnetic index prediction (MEng, current)
  • Speech and language applications of DNNs
    • ​Domain adaptation for speaker diarisation in low-resource environments (MEng, current)
    • Automatic speech recognition on poor quality audio using Generative Adversarial Networks (MEng, current)
    • Embedding recognised speech in a multilingual environment (MEng, 2020)
    • Classifying recognised speech with deep neural networks (MEng, 2020)
  • Other applications of DNNs
    • Development of a deep neural network framework for sailplane cross-country performance optimisation (PhD, current)
    • Whale call detection with deep neural networks (MEng, current)
    • Traffic flow prediction with graph convolutional networks in under-resourced environments (MEng, current)
    • Channel estimation and equalisation using generative adversarial networks (MEng, current)

Study leaders: 

Prof Marelie Davel, Prof Etienne Barnard & Dr Stefan Lotz

External domain experts participate as co-supervisors where relevant. Currently, the group includes the following experts:

Studies are also conducted in collaboration with other NWU faculty from the School of Electronic, Electrical and Computer Engineering, currently Prof Albert Helberg & Prof Alwyn Hoffman.

Excursion during 2019 study visit 1 to Hermanus   

Excursion during 2019 study visit 2 to Hermanus

   Post-colloquium beer tasting to celebrate 7 title registrations in 2019    Potchefstroom social event    Hermanus social event