17 May 2023 14:00 - 16:00 Alison Richard Building Foyer and SG1


This clinic is open to graduate students and staff at the University of Cambridge. It is running as a drop-in on Wednesday, 17th May from 2pm – 4pm in the Alison Richard Building Foyer and SG1 and you’re welcome to just turn up on the day. If you can’t make that time slot or you’d like to share some information in advance, please use this form to tell us more about your issues. Please note this form is set for University of Cambridge log-in only, please ensure you are logged onto your University Google Workspace


What kind of problems can you help me with?

We can offer advice related to proposal writing, software, hardware, data collection methods, data security, privacy and compliance, selection and deployment of ML models and packages and many other aspects of deploying Machine Learning in your research. You do not need to be currently working on a project using ML to access the clinic, we are also happy to take enquiries from researchers who are still at an early stage in project development.  We can also help you with suggestions about training courses and self-study resources.

While the Clinic aims to resolve all issues logged in its database, this might not always be possible, for example if the issue proves to be outside the current skillset of the Clinic’s engineers. In those cases, we’ll try to at least help researchers make progress on their issue by helping route it to other engineers in Cambridge who might be better placed in resolving it.

How long will it take to respond to my query?
We aim to resolve issues within two weeks of them being logged. However, this metric is highly variable and will depend on the volume and nature of issues we receive. We will notify you if the Clinic is experiencing any delays that prevents it from resolving your issue within two weeks.

Who gets to view my issue and how will the information I submit be stored or managed?

Issues that are logged at the clinic are stored on a spreadsheet that is shared with members of the Accelerate Science programme and CDH staff. We will ask for your permission to share your issue with other members of Cambridge University, for example if we think there are others who are better placed at the university to resolve your issue.

How should I acknowledge Accelerate Science’s contribution in publications or other materials if it provides useful advice on resolving my issue?

We hope that sometimes discussions held with the Clinic will evolve into genuine scientific collaboration, but in many cases an acknowledgement in your paper of the ML Clinic’s assistance will be fine and we’d be happy to advise on this further or provide suggested wording for acknowledgement in relation to specific projects.

Cambridge Digital Humanities

Tel: +44 1223 766886
Email enquiries@crassh.cam.ac.uk