Mark Turner

Software Engineering

Personal page

Research Interests

  • Data Integration

  • Data Science

  • Data Visualisation

  • Digital Health

  • Distributed Computing

  • e-Science

  • Interdisciplinary Computing

  • Open Data

  • Public Health

  • Scalability

  • Sensor Networks

  • Urban Systems

  • UX Design

  • Web Services

The Research Software Engineering team is offering three projects with real-world applications and the chance to work collaboratively alongside the team. Each project will be lead by a different member of the RSE team and co-supervised by Mark Turner.

Real-Time Car Parking - Andy Hardy A project to enable users to find their closest free parking space. The application will make use of the urban observatory data stream to visualise parking availability on a map in real-time. The application should work on all the standard devices from desktop to mobile but an emphasis should be made for mobile-first. If time allows enhancement could be to enable the application to use audio to notify the user of their nearest parking space and integrate with direction APIs to plot a route.

Senescent Cell Counting - Frances Hutchings It can be difficult to accurately detect the number of cells in a microscope image, particularly for cells with a large variance in shape and size. We have a machine learning algorithm, implemented in Python, trained on microscopy data which can output an estimated count. However, the current results are still not as accurate as we would like. This project will involve improving the current code in order to increase the counting accuracy. The student will learn about machine learning through working on this real-world application, which, if successful, will be used in the biology laboratories at Newcastle University.

Antarctic Coastline Mapping - David Herbert There are big challenges in keeping the ice coastline geospatial dataset of Antarctica up to date. The continent is rapidly changing, with bits breaking off all the time. Science and logistics in Antarctica depend on up-to-date data, and the Antarctic Digital Database catalogue aims to provide this. British Antarctic Survey (BAS) maintain this and spend a lot of manual effort digitising radar imagery from Sentinel satellites to keep the coastline dataset current. There is a real possibility that much of the digitisation work can be undertaken by automatic algorithms, with expert feedback helping digitisation software to learn the process and refine the success rate. This project would contain some image analysis and a machine learning component. GIS/geospatial software experience would be desirable.

Technologies & Programming Languages

I am comfortable supervising projects that make use of the following technologies and programming languages.

  • Angular

  • Bash/Shell

  • C#

  • Flask

  • GraphDB


  • Java

  • JavaScript

  • Laravel

  • Modern Web

  • NoSQL

  • PHP

  • Python

  • R

  • Scala

  • Spring

  • SQL

  • TypeScript

  • Vue.js



TODO: Use this site to find project supervisors for your masters projects.