AI intern

Key Skills

Active Learning algorithms, AI technology, Foundation Models, Generative AI

Job Description

The AstraZeneca Summer Internship Program

Location Cambridge, England, United Kingdom

10–12-week development experience running through the summer months each year

Internships are meant to be a catalyst in helping students gain hands on learning & exposure to the career they’re pursuing.

Internships benefit students by supporting them to:

• Build their professional network.
• Gain relevant experience for post-graduation opportunities.
• Expose them to AstraZeneca as a possible future employer.

Intern Eligibility Requirements:

• Students must be 18 years or older at time of hire; and not require sponsorship to work in the UK
• Students pursuing undergraduate, masters or doctoral degree.

A salary will be paid.

This proposal focuses on automatically segmenting diverse medical imaging modalities such as MRI, CT, and pathology through the combined use of Foundation models, Active Learning and Generative AI (Artificial Intelligence). Automatic segmentation helps to reduce the workload on domain experts, including radiologists, in vivo specialists, and pathologists. Training reliable models requires enormous amounts of domain experts’ annotations, which are costly and time-consuming.

Foundation models are primarily trained on natural images, and the project will look at using these models for multi-modal medical images by first enhancing them using active learning and generative AI. We hypothesise that foundation models learn underlying global structures, with transfer learning, using domain-specific images, helping to learn local structures. For example, human MRI and CT data are more widely available than rat and mouse data. Developing methods to transfer learning from human data to animal data would optimise animal use and may bridge the gap between preclinical and clinical research.

However, Foundation models are primarily trained on massive datasets, which are not always accessible. We will use a combination of active learning and generative AI to provide domain-specific images to fine-tune the pre-trained foundation models. Active learning will help determine which images need expert labelling, and generative AI will help the expert better enhance the resolution of specific images.

What are we looking for?

At AstraZeneca, we put patients first and strive to meet their unmet needs worldwide. We’re deeply committed to our strategy of being a great place to work. For us, that includes fostering an environment where everyone can be among inclusive, supportive individuals with curious minds.

A company that genuinely follows the science and values individuals at all levels, you’ll be backed and encouraged to speak up, ask questions, and share ideas to push the boundaries of science and continuously learn and explore.

Our purpose is bold and so is our approach. Becoming a more agile and innovative company means building a dynamic, inspiring culture where we celebrate entrepreneurial thinking and act with a sense of urgency. We are courageous, taking risks and learning from both success and failure. We are curious, creative, and open to new ideas and ways of working. Above all, we are passionate about science and driven to always put patients first. When you join us, you will be part of a great place to work; in an environment that energizes and empowers each of us to achieve our goal to develop and deliver medicines.

AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.