The MUST Deep Learning research group performs basic and applied research in machine learning, with an emphasis on the theory and application of deep learning. Our theoretical work studies the generalisation ability and interpretability of deep learning models from novel perspectives. Our application domains are diverse, currently ranging from environmental monitoring and space weather prediction, to industrial applications of deep learning. MUST is a student-centred environment where researchers are nurtured and challenged to produce research results that matter.

MUST (www.nwu.ac.za/must) forms part of the Faculty of Engineering at North-West University (NWU), South Africa. It is a node of the South African National Centre for Artificial Intelligence Research (CAIR), and the coordinator of the machine learning research programme of The National Institute for Theoretical and Computational Sciences (NITheCS). As such, the group develops and applies specialised machine learning techniques with a network of collaborating researchers.

PURPOSE OF THE POSITION

The incumbent will play a leading role in the research conducted in the MUST group and contribute to the overall success of the group. This includes participation in the group’s research programme through his/her research contributions, scholarly publications, postgraduate student supervision, and the execution of externally collaborative research projects.

JOB DESCRIPTION

KEY RESPONSIBILITIES:

Research

• Conduct high-impact research, aligned with the MUST Deep Learning research agenda.

Postgraduate student supervision

• Supervise Master’s and/or PhD students and/of postdoctoral fellows and/or interns. (Depending on appointment level)

Project contribution

• Contribute to specific research and development projects: from the initial sourcing of funds to project delivery. Depending on appointment level: either lead or contribute to research; lead or contribute to stakeholder interaction.

Management

• Contribute to the management of the MUST research group.

ADDED ADVANTAGE:

• Proven researcher (NRF rating)
• Registration as Professional Engineer
• A minimum of 3 years’ experience in deep learning-specific R&D a significant advantage.

KEY FUNCTIONAL/ TECHNICAL COMPETENCIES:

• Excellent research skills and a sound publication record.
• Proven competency in machine learning, deep learning and underpinning disciplines. 
• Strong programming skills, especially in Python and related machine learning libraries.

KEY BEHAVIOURAL COMPETENCIES:

• Strong research orientation. 
• Critical thinking. 
• Analytical and problem-solving skills.
• Outstanding communication, planning and organisational skills.
• Excellent work ethic, integrity and independence. 
• Self-motivated individual with strong interpersonal skills

REMUNERATION

The annual total remuneration package will be commensurate with the level of appointment as advertised and in line with the NWU policy guidelines. 

ENQUIRIES REGARDING JOB CONTENT MAY BE DIRECTED TO:                    Mrs A Geldenhuys: 067 203 8092

ENQUIRIES REGARDING RECRUITMENT PROCESS MAY BE DIRECTED TO:   Ms Fadilha Minty: 018 299 4993

CLOSING DATE:                                                                                                15 July  2024

PLANNED COMMENCEMENT OF DUTIES:                                                         As soon as possible

Kindly take note: applications must be submitted online through the official NWU vacancy website.

Incomplete applications and those submitted through any other platform will not be considered.

The University subscribes to and applies the principles of Employment Equity (EE) Act and is committed to transformation. Preference will be given to candidates from the designated groups, in accordance with the principles of the EE Act and NWU Employment Equity Plan. 

The University reserves the right not to make an appointment. Communication will be limited to shortlisted candidates only.

If you are not contacted within two months from the closing date of this advertisement, please accept that your application was unsuccessful.