Compass Guest Lecture: Dr Kamélia Daudel, Postdoctoral researcher Department of Statistics, University of Oxford

New Opportunity: Funded PhD Project on Developing methods for model selection in causal health analyses

How can statistical modelling tell us what causes disease? Electronic Health Records (EHR) have transformed medical research, with diverse examples including examining risks of emergency admissions on weekends vs weekdays, and health and psychological outcomes after COVID-19. The strengths of analyses based on EHR data include the very large number of individuals available for analysis and the extremely detailed data available for each individual.

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Access to Data Science: start your PhD journey with Compass

Want to find out what a modern PhD in Statistics and Data Science is like?

Access to Data Science provides an immersive experience for prospective PhD students. This fully-funded, two-day event will be hosted by Compass academics and PhD students in the Fry Building, home to the School of Mathematics at the University of Bristol.

Application deadline: Monday 18 October 2021

Event dates: Monday 8 November – Tuesday 9 November

Find out more about the event here

The purpose of this event is to increase all aspects of diversity amongst data science researchers. We particularly encourage applications from women and members of the LGBTQ+ and BAME communities to join us. 

             

What to expect from the Access to Data Science event:

  • attend seminars and guest lectures
  • take part in a hands-on workshop
  • have exclusive access to an application writing workshop
  • work with the current Compass PhD students
  • option to attend the Women and non-binary people in mathematics event.

Who can attend

We welcome participants from a range of numerate academic backgrounds, with undergraduate degrees in subjects such as computer science, economics, epidemiology, mathematics, statistics and physics.

We welcome applications from across the UK. Access to Data Science participants will be offered hotel accommodation, reimbursement of travel costs and meals for each day of the event.

Apply to Access to Data Science here 

Compass Away Day

At the end of July 2021, Compass students and staff travelled together to the Brecon Beacons National Park in Wales for a day full of adventure, which was carefully planned by Call of the Wild.

 

 

 

 

 

Activities on the day started with some fun team tasks called the ‘Mini Olympics’. Some of the tasks tested logical thinking, the ability to do a task under time pressure, or simply work as a team to complete a certain objective but ultimately to have fun and a laugh.

 

The tasks were a great opportunity to work together and get to know each other better. Some of them have been more difficult to complete than our students and staff initially expected, but very enjoyable.

 

 

After lunch Compass students and staff started a 3-hour Canyoning adventure, guided by the very well trained Call of the Wild team.

The best way of describing this canyoning activity is white water rafting but without the raft. With qualified guides, our students and staff descended a stunning steep sided gorge by various ways and means. This involved sliding down rapids, swimming down rapids, floating down fast flowing chutes and waves, walking behind some breathtaking waterfalls and of course jumping off some jaw dropping waterfalls.

 

 

After this thrilling adventure, Compass students and staff travelled to the Vale Resort where they enjoyed dinner together and leisure time until the day after when it was time to come back to Bristol.

It was wonderful to be able to spend time together after the long months of working from home.

 

 

 

 

 

 

 

Video: The Data Science behind COVID Modelling

We are excited to share Dr Daniel Lawson’s (Compass CDT Co-Director) latest video where he will tell you about the Data Science behind Bristol’s COVID Modelling.

Mathematics has had a hidden role in predicting how we can best fight COVID-19. How is mathematics used with data science and machine learning? Why is modelling epidemics such a hard problem? How can we do it better next time? What will data science be able to do in the future, and how do you become a part of it?

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