University of Glasgow | Funded PhD "Harnessing Large Language Models and Dimensionality Reduction to Reframe Student Feedback in UK Universities." | Glasgow, UK | Onsite
Join our fully funded PhD program: "Harnessing Large Language Models and Dimensionality Reduction to Reframe Student Feedback in UK Universities."
Run jointly by the James Watt School of Engineering and the School of Computer Science, this project will enable you to:
- Use AI and machine learning to analyse and improve student feedback.
- Collaborate with Student Voice (www.studentvoice.ai) for real-world impact.
- Gain expertise in advanced AI techniques.
I'm skeptical this would work with crawling for anything other than the most basic toy websites. How can this identify components that use generated class names (any css-in-js, css modules, etc)? Can it pull prop usage out?
I gave it a try on just our public login page and it said "no pages found" - perhaps it doesn't work for single page apps?
Ah interesting - and endorsed I see by Brad Frost too (although it's interesting he doesn't identify the issue in this Ecosystem post, so perhaps he does't see it as quote the holy grail, but hey).
Yes, sorry. The not-to-scale aspect meant it jumped the shark almost immediately for me as well. As small as possible, following the initial display of the object, might have conveyed the idea better - and that would have made the Chinese explosion a little more fun.
I understand the technical limitations, but it appears that you have an axe to grind: sounding the alarm about space debris. In that context it seems disingenuous to show space as more crowded than it is.
The debris is coloured to match the descriptive text.
Each years data is loaded when the user gets to that part of the visualisation.
The ring you see is indeed all of the satellites in geosynchronous orbit. You can also see the orbits of many of the GNSS satellites in medium Earth orbit if you stare hard enough for long enough!
This shows all objects (functioning and debris) in orbit at the time in question.
Fully funded PhD Studentship in Space Debris Environment Modelling using Spatio-Temporal Analysis
University College London
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Although largely unrecognised by the general population, the world has never been more reliant on access to space. However, continued use of the space environment over the last 60 years has polluted the orbits around the Earth with debris. Currently only a small fraction of space debris is tracked. Failure to capture this data more accurately, and model its behaviour, could result in us losing access to space entirely if a space debris cascade event is allowed to occur.
The aim of this PhD project is to describe the current space debris environment using cutting edge astrodynamics methods and model its evolution over time using machine learning techniques and spatio-temporal data analysis. The Space Geodesy and Navigation Laboratory (SGNL) has become a world leader in accurately modelling the many forces acting on spacecraft in orbit. Our models are currently employed by both NASA and ESA to supply data to support scientific environmental monitoring missions (JASON-1, SENTINEL). SpaceTimeLab have a wealth of experience in the analysis, modelling, simulation and visualisation of large spatio-temporal datasets, with applications in transportation, crime and disaster response. This is an exciting opportunity to build on these models and develop novel techniques to gain new insights into the behaviour of the space debris environment.
Skills you will learn include design, development and testing of algorithms, software and database development, experimental design and implementation, data analysis and both technical and interpersonal communication. You will also have the opportunity to build a network of contacts within both the astrodynamics and spatio-temporal data analysis communities.
This project is a collaboration between UCL’s Space Geodesy and Navigation Laboratory and SpaceTimeLab and will be supervised by Dr Stuart Grey and Dr James Haworth.
Eligibility: Applications are invited from UK and EU members, residing in UK.
Application Procedure: Applicants should send a covering letter and CV to s.grey@ucl.ac.uk
Start Date: The post will be available from September 2014
Duration: 4 Years
Funding: The scholarship covers UCL registration fees and provides a stipend of £15,726 per annum tax free. Travel expenses and research equipment will also be paid for.
Funding Body:EPSRC (Engineering and Physical Sciences Research Council)
Join our fully funded PhD program: "Harnessing Large Language Models and Dimensionality Reduction to Reframe Student Feedback in UK Universities."
Run jointly by the James Watt School of Engineering and the School of Computer Science, this project will enable you to:
- Use AI and machine learning to analyse and improve student feedback. - Collaborate with Student Voice (www.studentvoice.ai) for real-world impact. - Gain expertise in advanced AI techniques.
Deadline: June 21, 2024
Details: https://www.findaphd.com/phds/project/phd-in-engineering-har...