The Sustainable and Healthy Food Systems – Southern Africa (SHEFS-SA) consortium is a partnership between institutions in Malawi (MUBAS and LUANAR), South Africa (University of KwaZulu-Natal and the Institute of Natural Resources), Zimbabwe (University of Zimbabwe) and the United Kingdom (London School of Hygiene & Tropical Medicine, University of Aberdeen, Royal Veterinary College and University of London). The consortium is working to catalyse the transformation of Southern African food systems (focusing on Malawi, South Africa and Zimbabwe) and communities towards systems and communities that are healthy and resilient to climate risks. We will do this by: 

• undertaking SHEFS transdisciplinary research to shift the understanding of complex climate change (CC) challenges for health, as mediated by food systems, within particular contexts, translated into scalable solutions and policy recommendations with high impact;
• developing a transdisciplinary Community of Practice (CoP), led by the Global South, that contextualises and applies systems thinking within an expanded climate-sensitive SHEFS Framework to improve food security, food safety, nutrition, and health, including mental health; and (iii) developing a Global South-led cohort programme to train emerging scholars and practitioners in transdisciplinary research approaches at the intersection of Climate and Health.

The SHEFS-SA consortium will focus on providing actionable evidence for informed decision-making and identifying and developing practical solutions for CC mitigation and/or adaptation while evaluating in detail how their effects connect to health, including mental health, through food security, food safety and nutrition. The programme deepens our work in South Africa and will expand to include Zimbabwe and Malawi to ensure regional policy impact.

We seek a dynamic Research Assistant to join our cutting-edge project focusing on using AI techniques to assess infrastructure resilience and develop predictive maintenance models.

Key Responsibilities

• Conduct research on infrastructure resilience assessment methodologies;
• Develop AI-based models for predictive maintenance of critical infrastructure systems;
• Analyse structural and operational data to identify vulnerabilities and predict failures;
• Collaborate with interdisciplinary teams, including engineers, data scientists, and urban planners; and
• Contribute to the preparation of technical reports, research papers, and presentations.

Qualifications

• BSc or MSc in Civil Engineering, Computer Science, Data Science, or a related field;
• Strong programming skills in Python, R, or MATLAB, with experience in AI/M; frameworks (e.g., TensorFlow, PyTorch, Scikit-learn);
• Familiarity with infrastructure systems and resilience assessment methodologies;
• Experience in data analysis, visualisation, and statistical modelling; and
• Excellent problem-solving, organisational, and communication skills.

Preferred Skills

• Knowledge of structural health monitoring and predictive analytics;
• Experience with large datasets, IoT sensor data, or real-time monitoring systems;
• Familiarity with GIS tools and spatial data analysis; and
• Research experience in AI applications to civil or structural engineering challenges.

Duration:

This position is tenable for a duration of one year.

How to apply:

Submit your CV, a cover letter outlining your research experience and motivation, and contact details for at least two referees through email to ckasonda@mubas.ac.mw with a copy to arhrm@mubas.ac.mw.