Project description and scope of internship:
The International Water Management Institute (IWMI), is looking for enthusiastic students to tackle climate change challenges on food, water and health through research and development under the Institute’s internship program. The interns will work closely with CGIAR scientists and country partners to get exposure and enhance their skills. Through this internship, enthusiastic individuals will be able to develop a machine learning-based early warning system that enables accurate health, food price, nutrition, and human displacement predictions for anticipatory disaster mitigation responses. The CGIAR Initiative on Climate Resilience (ClimBeR) will work closely with country partners in Senegal, Zambia, Guatemala, Kenya, Morocco, and the Philippines to implement Early Warning to Early Action and Early Finance “AWARE” platform. The research interns will work closely with a team of researchers on a range of activities, including the following:
During this internship, the intern will assist in the following areas:
- Study and transform data science prototypes that are of relevance to the ClimBeR initiative.
- Select relevant datasets and data representation methods.
- Design machine learning systems.
- Conduct research and implement suitable machine learning algorithms and tools.
- Develop machine learning models in accordance with the requirements of the ClimBeR initiative.
- Transforming code and running several machine learning tests and experiments.
- Test, train and retrain machine learning models where necessary.
- Conduct model validation and error estimation using statistical analysis and refine test results.
- Expand existing machine learning libraries and frameworks to evaluate climate shocks.
Qualifications, knowledge and experience
- Master’s degree or working on doctoral degree in Computer Science, Engineering, Applied Mathematics or Natural Sciences, or an equivalent qualification.
- Good knowledge of mathematics and statistics, especially in probability functions and logical algorithms.
- Ability to write robust code in Python, with knowledge of Java, FORTRAN or R is desirable.
- Familiarity with machine learning frameworks (e.g., TensorFlow, Keras or PyTorch) and libraries (e.g., scikit-learn).
- Ability to work within a team and have problem-solving skills.
The duration of the initial internship contract will be for 6 months, either full-time or part-time. The level of effort for a part-time internship is approximately 20 hours per week, but full-time is preferable. The tentative start date is as soon as possible. The interns will be based at one of IWMI’s offices in India or at its headquarters in Colombo, Sri Lanka.
IWMI will provide a monthly stipend to the selected candidate during the internship period.
To apply, visit www.iwmi.org/jobs. Applications must be submitted by 24:00 (NST) on February 09, 2023 (Thursday).
The International Water Management Institute (IWMI) is an international, research-for-development organization that works with governments, civil society and the private sector to solve water problems in developing countries and scale up solutions. Through partnership, IWMI combines research on the sustainable use of water and land resources, knowledge services and products with capacity strengthening, dialogue and policy analysis to support implementation of water management solutions for agriculture, ecosystems, climate change and inclusive economic growth. Headquartered in Colombo, Sri Lanka, IWMI is a CGIAR Research Center with offices in 14 countries and a global network of scientists operating in more than 30 countries. www.iwmi.org
IWMI believes that diversity powers our innovation, contributes to our excellence, and is critical for our mission. We offer a multi-cultural, multi-color, multi-generational and multi-disciplinary working environment. We are consciously creating an inclusive organization that reflects our global character and our commitment to gender equity. We, therefore, encourage applicants from all cultures, races, ethnicities, religions, sexes, national or regional origins, ages, disability status, sexual orientations, and gender identities.