AddressThe AstraZeneca Summer Internship Program
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 project aims to extend recently developed Textual Inversion methods, including the Multi-Concept Prompt Learning (MCPL) framework, from the text-image setting to the protein sequence-structure setting. Textual Inversion [1] learns to represent a concept or style in an image as a new “pseudo-word” in the embedding space of a frozen text-to-image model. These soft embeddings can then be composed into new language prompts and used to generate novel images. MCPL [2] builds on this method to enable simultaneously learning multiple embeddings which represent distinct “concepts,” potentially associated with different parts of an image. We propose modifying this framework to function in the case of proteins and antibodies where the structure pays the role of an image and the sequence plays the role of the text. Using inversion we will invert local structural information to sequence embeddings. This will allow for manipulating and editing a learned sequence embedding and generating an ensemble of structures for regions of interest within an overall structure.
Key technical skills or xperience required
- Experience working with and implementing deep learning models, generative models, eg, diffusion models
- Good Programming skills
- Experience working with PyTorch
Primary duties of this position
- Conduct cutting edge research focused on protein and antibody design, and other problems in drug discovery
- Work closely with AstraZeneca AI/ML scientists and engineers, and other scientists at AZ, to develop new machine learning models
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.
