Data Science Internships
LIDA is offering internships to help to drive research activity across each of LIDA’s constituent areas: medical bioinformatics, consumer data research and multidisciplinary data analytics.
The LIDA internship programme provides an outstanding opportunity to develop skills that are in high demand, both nationally and globally, within a new, state-of-the-art facility. Our offer to interns includes the chance to:
• Own delivery of a data science project and get hands-on technical experience using real data;
• Establish links with project partners (internal or external) and work to provide solutions to real world challenges;
• Build skills and knowledge in advanced analytics, not only through mentorship and applied research, but also through on-site training opportunities in statistical analysis, visualisation, research methods and computer programming;
• Work alongside leading scholars as part of the LIDA team and gain valuable work experience.
What does the role entail?
As a Data Scientist Intern, your main duties will include:
• Helping to drive research activity across each of LIDA’s constituent areas: medical bioinformatics, consumer data research and multidisciplinary data analytics;
• Undertaking quantitative analysis on core datasets;
• Planning own research activity in collaboration with the project investigators and other members of the research team;
• Meeting with clients and partners in support of LIDA’s partnership-building efforts;
• Participate in LIDA research meetings and seminars;
• Contribute to written outputs, including research papers, case studies and other materials for publication
Information on our current interns and the projects they have been working on can be found here.
Please click ‘more’ to view full details of the role and apply. The deadline for applications is Monday 23rd July.
Agent-Based Modelling of Smart Cities
This PhD project is part of an exciting new initiative entitled “Data Assimilation for Agent-Based Models (DUST)” The project will develop important new methods that can be used to integrate data that emerge from smart cities (e.g. from traffic counters, social media activity, environment sensors, etc.) into largescale urban simulations in real time. Without the ability to integrate real-time data streams into models, it is very difficult to understand how to manage cities in the short-term, or how to respond in situations that require dramatic interventions such as an emergency evacuation. The successful applicant will join a team of PhD students, data scientists and researchers at the Leeds Institute for Data Analytics who are working together to improve the ways that scientists can model cities.
More specifically, the successful candidate for this project will develop a large-scale model of urban flows – i.e. a model of the movement of people as they travel around a city. In particular, the project will explore the potential uses of such a model in areas such as urban management (can models help us to better understand how spaces
are being used?) and evacuation planning (by better understanding urban flows in normal circumstances, can we develop better plans for emergencies that require city-centre evacuation?). An important aspect of the work will be how ‘big data’, that are generated in abundance in smart cities, can be used to improve the realism of simulations. Here, methods from data science and urban analytics will be invaluable. Models such as the one produced by this PhD project could be extremely valuable because they will help scientists and policy makers to better understand the daily pulse of a city, and to then find way to make cities better by (for example) reducing pollution in the places where most people are exposed, or better understanding where crime rates are significantly higher than elsewhere.
Funding and Eligibility
The project is fully funded! This includes:
– Tuition Fees
– Stipend (maintenance) ~£15,000 pa
– Research & Training grant: £750 pa
Total value: approx £84,000
The position is open to all UK/EU citizens. The following experience / training is required:
– A good undergraduate degree in computer
science, geography, mathematics, or a related
– Some experience in computer programming
For more information please contact the primary supervisor Nick Malleson.
Please click ‘more’ to view full details of the role and apply.More