Machine Learning Research for Document Intelligence

Volunteering

Overview

Contribute to research in machine learning and intelligent document processing.

You have:

• A Master’s degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, or Natural Language Processing is a prerequisite. A PhD degree in a related field is highly advantageous.
• A strong mathematical foundation is preferred.
• Proficiency in Python programming is required, along with experience using relevant deep learning frameworks, such as PyTorch (preferred), TensorFlow, or JAX and HuggingFace. Hands-on experience with DGL and PyG is an asset.
• Demonstrated knowledge of research in Intelligent Document Processing, Document Intelligence, Semantic Layout Analysis and broader areas of Computer Vision, Natural Language Processing, and Geometric Machine Learning.
• Research experience or publications in a related field is a strong asset.
• Proficiency in English is required. Working knowledge of French or Spanish is an asset.

Contract

This is a UNV contract. More about UNV contracts.

The SDG Integration Team located with UNDP’s Global Policy Network (GPN) offers a menu of services emphasizing direct short- to medium-term engagements to respond rapidly to requests from country offices for support on national implementation and monitoring of integrated policy solutions, qualitative and evidence-driven analysis for accelerated progress, and knowledge sharing and upscaling of innovative approaches to sustainable development. The team’s work emphasizes the application of evidence-driven data and analytics for SDG implementation and reporting. In this regard, advances in digital technology are creating data at unprecedented levels of detail and speed, turning the stories of people’s lives into numbers every minute of every day, across the globe. An important focus of the integration work is to complement traditional data (e.g., national statistics,) with new and alternative sources including digital ‘breadcrumbs,’ satellite data, social media to identify emerging trends and gain new perspectives on issues in development.

The data team at the BPPS is looking for a skilled and enthusiastic Online Volunteer with documented experience in machine learning (ML) to contribute to our ongoing research in intelligent document processing (IDP).

The selected Volunteer will work under the guidance of the Data Science Specialist to:

– Conduct a desk review of literature, frameworks, and advancements in the field of IDP. – Prototype machine learning (ML) models for semantic document layout analysis using provided datasets. – Write up the research and results of experimental work into a short research paper.

Note that this is a highly selective opportunity. Your application must include a short motivation statement that clearly describes how your education and experience align with the task. Please include links to your GitHub, publications or online portfolio, if applicable.

To be considered for this highly selective role, candidates must meet the following qualifications and skills:

– A Master’s degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, or Natural Language Processing is a prerequisite. A PhD degree in a related field is highly advantageous. – A strong mathematical foundation is preferred. – Proficiency in Python programming is required, along with experience using relevant deep learning frameworks, such as PyTorch (preferred), TensorFlow, or JAX and HuggingFace. Hands-on experience with DGL and PyG is an asset. – Demonstrated knowledge of research in Intelligent Document Processing, Document Intelligence, Semantic Layout Analysis and broader areas of Computer Vision, Natural Language Processing, and Geometric Machine Learning. – Research experience or publications in a related field is a strong asset. Candidates with a documented history of academic or industry-level work in these areas will be given priority. – Proficiency in English is required. Working knowledge of French or Spanish is an asset.

Apply now

Potential interview questions

Added 10 months ago - Updated 9 months ago - Source: unv.org