Software Engineering

Machine learning has become an indispensable tool for researchers across disciplines, but the software engineering knowhow required for supporting researchers is in short supply. Accelerate Science will help fill this gap.

As machine learning is adopted across disciplines, scientists are faced with new software challenges related to implementing and maintaining machine learning models. These challenges are becoming a bottleneck to progress. In response, Accelerate Science’s software engineering programme will offer tailored software support to unblock these bottleneck.

AI Clinic: Accelerate’s AI Clinic offers expert advice to Cambridge University researchers using AI in their research, helping resolve engineering issues they might encounter when implementing machine learning methods.

AI Clinic

Welcome to the Accelerate Science AI Clinic! This new initiative seeks to support Cambridge University researchers using AI in their research, by helping resolve engineering issues they might encounter when implementing machine learning methods.

Data Engineering School

Well-curated and managed data is central to the effective use of AI, in science and elsewhere. How can scientists build the data pipelines they need to accelerate their research with AI?

Related Resources

Python Programming For Science

This self-learning module introduces the fundamentals of Python, some of the kinds of data it can handle, and how to store that data. Designed for researchers across disciplines, it supports learners to rapidly learn how to code in the context of working with real-world data. To access the module, register at the link.

Introducing Data Science for Research

The Accelerate Programme offers PhDs and postdocs disciplines across Cambridge University the opportunity to participate in a ‘Data for Science’ training course. This structured Accelerate-Cambridge Spark Introducing Data Science for Research will equips scientists with modern practical data analysis skills using Python in a virtual instructor-led accelerated masterclass.

Machine Learning for Science Jupyter Notebooks

This gallery brings together Jupyter notebooks produced by participants in the Data Science for Science Residency. They contain information and code about the data science techniques that participants have used in research areas that range from genetics to astronomy.

AI and Large Language Models

A collection of resources for researchers interested in using Large Language Models (LLMs) in their research.

Accelerate has released code for working with Large Language Models in research; you can find it in our large-language-models GitHub repository. This code covers a range of ways to use and tune LLMs, including calling APIs, finetuning models, and creating more complex solutions like Retrieval Augmented Generation (RAG). This code is freely available for researchers to build on in their work.

Software Engineering

Machine learning has become an indispensable tool for researchers across disciplines, but the software engineering knowhow required for supporting researchers is in short supply. Accelerate Science will help fill this gap.

AI Clinic

Data Engineering School

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Better, faster and lighter diffusion models for medical diagnosis

Exploring the use of LLMs in humanities and social sciences research

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How can we… use AI to predict the outcome of court cases?

Related Resources

Python Programming For Science

Introducing Data Science for Research

Machine Learning for Science Jupyter Notebooks

AI and Large Language Models