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 motivated Research Assistant to join our research team in exploring innovative approaches to assess and model groundwater depletion using GRACE satellite data and advanced machine-learning techniques.

Key Responsibilities

• Process and analyse GRACE satellite data to monitor groundwater level fluctuation;
• Develop and apply machine learning models for groundwater depletion assessment;
• Conduct spatial and temporal analysis of groundwater trends and identify key influencing factors;
• Collaborate with a multidisciplinary team of researchers in hydrology, data science, and environmental studies; and
• Prepare reports, visualisations, and presentations for publications and project stakeholders.

Qualifications

• BSc or MSc in Hydrology, Environmental Science, Data Science, Geophysics, or a related field;
• Proficiency in working with Remote Sensing/GIS datasets, particularly GRACE data;
• Strong programming skills in Python, R, or MATLAB, with experience in machine learning frameworks (e.g., TensorFlow, Scikit-learn);
• Experience with geospatial tools such as ArcGIS, QGIS, or Google Earth Engine; and
• Excellent organisational, analytical, and communication skills.

Preferred Skills

• Knowledge of groundwater hydrology and water resource management;
• Familiarity with time-series analysis and predictive modelling; and
• Prior research experience in satellite data analysis or machine learning applications.

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.