Description

At Symbolica, we are building deep learning models which perform structured reasoning: manipulate structured data, learn algebraic structure in it, and do so with an interpretable and verifiable logic. To that end, we are developing new mathematical foundations for deep learning: categorical deep learning. We are now assembling a R&D lab of expert category theory and machine learning researchers to develop this theory and apply it to the problems of code synthesis and theorem proving. We are committed to fundamental ideas, but also their execution in practice.

As a Research Intern, you will help us expand and carry out our research & development program. You will work in a team on a project developing categorical deep learning and implementing it in models. This is an opportunity to be part of a transformative project and make significant contributions to the field of applied category theory and artificial intelligence.

Responsibilities:

• Help develop our categorical deep learning research programme
• Conduct applied category theory research in collaboration with a team, and contribute to high quality results on project timelines
• Understand state of the art category theory research and help us translate relevant insights into the field of deep learning
• Work simultaneously at different levels of abstraction - from high-level categorical constructions to low-level details of architecture implementation in code

Preferred qualifications:

• PhD in Mathematics, Computer Science or similar discipline (or nearing of completion of one),
• Publications in applied category theory, pure mathematics or machine learning venues
• Experience in category theory or type theory, and a strong interest in machine learning, or experience in machine learning and a strong interest in category theory and type theory
• Exceptional communication and interpersonal skills.

Location:

• London

Duration:

• 3-6 months

Salary:

• £50K per annum

Applications will be processed on a rolling basis, and closing date is 10th of June.