Overview

We are currently inviting applications for an internship position focused on building an agentic system for cultural tailoring of multi-modal content for the African Health Stories project. Africa Health Stories is a collaborative project between Microsoft Research and  Stellenbosch University, the University of Pretoria and Swansea University to create a story generation tool which produces interactive visual, oral, and text stories enabling patients to better understand how to apply health advice to their local circumstances.

This role presents a unique opportunity to contribute to the development of Machine Learning (ML) and Image Generation technologies that are culturally and linguistically grounded in the African context.

Key Responsibilities: 

• Participate in the African Health Stories project focused on leveraging generative AI to produce multi-modal content which is medically accurate and culturally appropriate to help patients living with Type II Diabetes make appropriate lifestyle changes.
• Develop agentic solutions for producing and evaluating culturally tailored and medically accurate output.
• Contribute to advancing Microsoft Research Africa, Nairobi’s human-centred AI (HCAI) agenda by experimenting with new methods for scalable human-centred evaluation of generative AI outputs.
• Publish findings in top conferences or journals.
• Engage in close collaboration with an interdisciplinary team comprising Researchers, Applied Scientists, Designers, and Machine Learning Engineers across various Microsoft Research teams to drive innovation and facilitate knowledge sharing.

Internship Duration: 6 Months 

Locations: Nairobi, Kenya Africa

Qualifications

Required: 

• PhD in Computer Science, Machine Learning, Statistics, Engineering, Mathematics, Physics, or related field or equivalent experience.
• Enrolled in, or recently completed, doctoral studies in Computer Science, Machine Learning, or related field.
• Knowledge of state-of-the-art and emerging Language Models, including their application in complex systems.
• Familiarity with Large Language Models such as the OpenAI GPT models, Phi3.5 etc., model fine-tuning techniques (LoRa, QLoRa), and quantization, prompting techniques (Chain of Thought, ReACT etc.), and Retrieval Augment Generation
• Coding and engineering skills to design and run experiments and help to drive research into product.

Preferred: 

• Strong understanding of foundational LLM architectures and optimization techniques
• Experience with multimodal models and AI agentic systems.
• A passion for bridging research and real-world impact, with a desire to see innovations translated into practical solutions.
• Publication record in relevant conferences, such as ACL, EMNLP, NeurIPS, ICLR, ICML, etc.