Microsoft Applied Science PhD Internship Opportunities 

Taiwan, Taoyuan City 

322459114

Start Date:July 2026

Internship (40hrs/week)
12-weeks

As an Applied Science PhD Intern at Microsoft, you will be at the forefront of technological innovation, working collaboratively to bring research to life in our products and services. Yand achieve your goals. This is your chance to bring your solutions and ideas to life while working ontechnology. This is a flexible work opportunity where you will be expected to work 3 days from our London Paddington office.

Required Qualifications

• Currently pursuing a Doctorate Degree in Computer Science, Artificial Intelligence, MachineLearningor a related field.
• Excellent problem solving and data analysis skills, with expertise in developing or applying predictive analytics, statistical modelling, data mining, or machine learning algorithms, especially at scale.
• Excellent verbal and written communication skills, with the ability to simplify and explain complex ideas.
• You must be legally authorised to work in the United Kingdom to be eligible for this role (Legally authorised = has citizenship or has been granted a valid visa or work permit)
• Fluency in English

Preferred Qualifications

• A publication record in top AI venues.

• Experience in the application of Language Models and Transformers.

• Apply or developstate-of-the-arttechniques to advance the capabilities of Microsoft 365 Copilot, with a particular emphasis on graph-based information retrieval,contextualizationand personalization, LLM prompt engineering, LLM post-training, synthetic data generation, and LLM-based evaluation.

• Gain a deep understanding of your area of research and applicable research techniques, as well as a basic knowledge of industry trends and share your knowledge with immediate team members.

• state of the art

• Systematically document and analyze proposed methodologies with the aim of publishing in a highly regarded AI research venues

• with the development of usable datasets for modeling purposes and support the scaling of feature ideation and data preparation.