AI Hopes and Fears
7 November 2023
The next Data Science for Science Residency will run for five weeks from Monday 27 February 2023. Applications are now open and close at 5pm on Friday 13 January.
The course aims to give researchers in fields outside computer science the skills they need to use machine learning (ML) in their research and helps them apply data analysis to their own datasets and problems. Over 150 learners from over 30 University Departments have completed the Residency over the past two years from a range of research areas including chemistry, biochemistry, physics, engineering, medicine, veterinary medicine and psychology.
From using AI to understand acute respiratory distress syndrome, take bio-imaging to the next dimension and enabling doctors to build their own models with clinical data our participants have come from a range of backgrounds to learn key skills in Machine Learning to apply to their research.
Watch our short video here where participants talk about their experiences on the programme and how it has helped them in their research.
Gaining a new perspective
Ryan Geiser, a PhD student in the Yusuf Hamied Department of Chemistry says the course has assisted his research in a number of ways: “Without the knowledge that I took away from the Residency, I would have struggled to conduct any data analysis this past year. In particular, I found coding exercises through the EDUKATE.AI platform invaluable in getting comfortable with the basic skills in data analysis and machine learning. You can read more about Ryan’s research and how the course has helped him on our blog.
Using Machine Learning to understand Acute Respiratory Distress Syndrome
Another previous participant in the Residency course is Dr Romit Samanta, NIHR Clinical Lecturer in Intensive Care Medicine. Romit works on Acute Respiratory Distress Syndrome (ARDS) and has been applying his learning from the course to understand patient outcomes. Romit says that “learning Python has enabled me to analyse a vast amount of data from ARDS and sepsis studies in the UK, as well as randomised control trials, using data science techniques to identify different subgroups of patients and understand how their biology relates to disease progression.” You can read Romit’s blog about his experiences here.
The Data Science for Science training course is part of the Accelerate Programme for Scientific Discovery. This is being funded by a generous donation to the University from Schmidt Futures. To produce this course, the Accelerate Programme is working with Cambridge Spark, an education technology company that specialises in data science and AI training.
Data for Science course participants should be current PhD students or researchers at the University of Cambridge and should have basic programming skills. Applicants are asked to commit to dedicating at least 30 hours per week to the course over the five weeks from Monday 27 February. Places are limited and participants will be selected based on a technical assessment and alignment with the aims of the programme.
The total cost of the course is £2,100 + VAT. The Accelerate Programme are offering part funding of the course for this cohort and will provide funding for 50% of the fees, meaning the cost to participants is £1,050 + VAT.