Message from the MIST mailing list.

Dear All, 

Please share the following PhD project with interested students. Funding is available on a competitive basis.

Informal enquiries can be sent to myself ([log in to unmask]) and Chris Arridge ([log in to unmask]).

Data Science in Giant Planet Magnetospheres

Each of the giant planets is surrounded by a planetary magnetosphere which contains a complex web of interacting elements, from a planet's neutral and ionised atmosphere, ring systems and natural satellites, populations of dust, neutral gas, plasma, and radiation belts, all embedded within the supersonic solar wind. Unravelling how these elements interact and what physical processes are at work over extremely large (million km) length scales is a formidable challenge and has been studied for the last four decades, starting with the Pioneer 10 flyby of Jupiter. The challenges of understanding these systems include processing and relating 100s of GB of heterogeneous datasets; accounting for sampling, resolution and other instrumental biases; and inferring the state of processes in large-scale systems with limited spacecraft trajectories. These are typical problems that are solved every day in the rapidly growing field of Data Science and which may enable a revolution in how we study planetary magnetospheres.

In this project, the student will study Saturn's magnetosphere using data from the entire Cassini mission. In particular, relationships between in situ measurements (plasma, energetic particles, magnetic fields) and remote-sensing measurements (energetic neutral atoms, aurorae) will be used to infer the state of the magnetosphere, and the physical processes at work, using a combination of machine learning and Bayesian inference. Of particular interest is how the natural satellites, rings, neutral gas and plasma interact with the planet's atmosphere, and how these elements interact with the external boundary conditions provided by the solar wind.

Project goals

--Use machine learning to automatically recognise structures and events from in situ field and plasma measurements from the Cassini spacecraft.
--Critically examine the efficacy of machine learning methods for automated magnetospheric data mining.
--Apply methods from Bayesian statistics to interpret observations and critically examine physically-motivated models for dynamics in giant planet magnetospheres.

Applications can be made here: http://www.lancaster.ac.uk/physics/study/phd/

Many thanks, 

Licia

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