Global Health Access Researcher at Microsoft

About the job
Research Internships at Microsoft provide a dynamic environment for research careers with a network of world-class research labs led by globally recognized scientists and engineers. Our researchers and engineers pursue innovation in a range of scientific and technical disciplines to help solve complex challenges in diverse fields, including health delivery and health discovery.

The widespread adoption of mobile technology in the Global South holds promise to increase meaningful access to quality healthcare, especially for disenfranchised populations. Mobile devices have already scaled how healthcare is delivered through enabling remote access to health information and services. AI can play a key role in this transformation by aiding to discern knowledge from data and separate signal from noise to better serve the needs of more people.

The Microsoft Africa Research Institute (MARI) and Health Futures - Global Health Access are interdisciplinary teams of scientists, engineers, and medical doctors who endeavour to increase meaningful access to quality healthcare and to ensure global access to precision medicine. We offer a unique vibrant environment that features cutting-edge academic research, enterprise software development, and real-world delivery, with close feedback loops and rapid iterations among all three.

We are looking for an innovative, collaborative, and highly motivated research intern to join our efforts in machine reading, where we partner with premier medical centers and develop state-of-the-art deep learning and natural language processing technology to extract knowledge from data and distil actionable insights that have a direct impact on healthcare access and equity.

Responsibilities
Interns put inquiry and theory into practice. Alongside fellow interns and some of the world’s best researchers, interns learn, collaborate, and network for life. Interns not only advance their own careers, but they also contribute to exciting research and development strides. During the 12-week internship, students are paired with mentors and are expected to collaborate with other interns and researchers, present findings, and contribute to the vibrant life of the community. Research internships are available in all areas of research, and are offered year-round, though they typically begin in the summer.

The ideal candidate will collaborate with a team of top-notch scientists, engineers, and biomedical practitioners to:

1. Analyze real-world data to explore opportunities for modelling and indirect supervision
2. Design and implement state-of-the-art systems
3. Conduct rapid experimentation and deep engagement for real-world delivery and impact
4. Propose novel approaches for unsolved research problems

Qualifications
In addition to the qualifications below, you’ll need to submit a minimum of two reference letters for this position. After you submit your application, a request for letters may be sent to your list of references on your behalf. Note that reference letters cannot be requested until after you have submitted your application, and furthermore, that they might not be automatically requested for all candidates. You may wish to alert your letter writers in advance, so they will be ready to submit your letter.

Required Qualifications

1. Currently enrolled in a PhD program in Computer Science, or a related STEM field, or MSc Students in Computer Science or related STEM fields, with experience and expertise in Machine Learning, Optimization and NLP.
2. 2+ years’ experience with machine learning, natural language processing, or a related field.
3. Participation in the Research Internship Program requires that you are physically located in Nairobi, Kenya for the duration of the internship.

Preferred Qualifications

1. Passionate about real-world applications and impact
2. Strong verbal and writing communication skills
3. Track record of publications in top ML or NLP venues
4. Experience with large datasets and distributed computing
5. Experience with deep learning platforms, such as PyTorch, Tensor Flow, CNTK
6. Experience in working with biomedical data a plus; willingness to learn a must!