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Mission and objectives
The UNDP Sustainable Energy Hub is a network of partners that work alongside countries to transform energy systems though an integrated agenda focused on the policy, technology and financial shifts that shape sustainable economic development. We help countries build net-zero, people-centered societies driven by a just, sustainable energy transition. Our core principle is to promote an integrated agenda that supports energy for development, including by mobilizing partners to enable 500 million additional people to have access to sustainable, reliable, affordable energy by 2025, leaving no-one behind. The global transformation of energy systems has already started, but is being altered by the current geopolitical context. However, this transformation must be accelerated, and it must be done in a way that advances the Sustainable Development Goals. The Sustainable Energy Hub is UNDP’s answer to these challenges. To drive the systems-level change needed, the Sustainable Energy Hub aims to bring about a completely new way of thinking, doing business, connecting people and knowledge. UNDP’s 2022-2025 Strategic Plan has put sustainable energy at the heart of a joint corporate mission, and UN-Energy has pledged to reaching key energy milestones by 2025. UNDP will focus efforts on mobilizing partners and catalzing action to provide access to sustainable, affordable, and reliable energy – both electricity and clean cooking – to 500 million people by 2025, focusing on the world’s poorest communities. UNDP will not do this alone – our role here is to mobilize strong, meaningful, impactful partnerships to deliver action on the ground. We aim to bring about a new way of thinking about energy and advocate for an integrated, inclusive approach, where all stakeholders participate meaningfully in its design.
Context
In the pursuit of sustainable energy solutions and informed decision-making, the Sustainable Energy AI in development by the UNDP Sustainable Energy Hub combines recent advances in LLMs with a robust energy knowledge graph to create a powerful new tool to support access to insights on energy. The Energy AI synthesizes insights from a comprehensive database of compiled publications, national policies, real-time APIs, and a database of indicators. This approach can provide access to knowledge and intelligence which can empower policymakers, researchers, and development practitioners with accurate, transparent, and contextually rich information for technical capacity-building and data-driven decision-making. The AI is integrated directly with a knowledge graph trained on our database of publications and documents on sustainable energy, and accessible through our interface within our new Sustainable Energy Academy currently in development. The Knowledge Graph needs to be further advanced especially by layering in additional documents and datasets, and improving the scope and utility of the information within so it can provide insights and resources to stakeholders and development practitioners working in sustainable energy.
Task Description
This online volunteer role will include contribution to the Sustainable Energy AI initiative using NLP and data science techniques, including specifically in refining our knowledge graph on Sustainable Energy. This will include data processing on our curated collection of documents to improve the data pipeline for developing definitions and mapping resources and data to entities and relations within the graph. The result of the task will include a completed version of the knowledge graph, developed jointly with the team. The assignment scope will include advancing the quality and extent of the knowledge graph by improving specific functions and processing pipelines. Additional support included in this scope includes to refine the text database by improving the semantic cleaning functions for parsing the text dataset, and to make better use of the metadata attached to the document collection to develop a more nuanced system for integrating information across diverse text data sources. This support includes two assignments, one of which will focus on improving the document collection itself, and one which will focus directly on the knowledge graph generation pipeline.
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