UCL Department of Space and Climate Physics


STFC PhD Projects 2020

All STFC funded studentships starting in 2020 have been filled. PhD projects in astrophysics, planetary science and space plasma/solar physics below are just for an information.

Our STFC studentships starting in 2020 have been filled. Below is just to provide an information about the  research area of our PhD projects. 

For more information on the application process, please visit the UCL Graduate Degrees pages and read the "guidelines for research programmes" carefully. To apply, please visit the Online Application page, select department of "Space & Climate Physics" and programme type of "Postgraduate Research". After pushing "Search Now" button, select "RRDSPSSING01: Research Degree: Space and Climate Physics" for Full-time or Part-time mode. 

Entry requirements

An upper second-class Bachelor’s degree, or a second-class Bachelor’s degree together with a Master's degree from a UK university in a relevant subject, or an equivalent overseas qualification.

Students from the UK or those from the EU who meet the residency requirements (3 years' full-time residency in the UK) are potentially eligible for a Science and Technology Facilities Council (STFC) studentship.

Additional eligibility requirements

These pay UK/EU tuition fees and a maintenance allowance for 3.5 years (subject to the PhD upgrade review).

EU students who do not meet eligibility requirements still qualify for the UK/EU fees rate, but not the STFC maintenance allowance.

Solar Accelerated Electron Beam-Plasma Interactions in our Solar System

Supervisor: Dr Hamish Reid

Our Sun’s outer atmosphere is extremely unstable.  Frequent explosions accelerate particle beams to near-light speeds, and periodically expel huge volumes of mass, known as solar storms.  The effect of our active Sun on the Earth is known as space weather.   Understanding the acceleration and transport of these particle beams is an important, modern challenge, previously hindered by a lack of spacecraft measurements near the Sun.  The goal of this project is to understand how these particle beams evolve on their journey through the solar system using a combined programme of data from cutting-edge spacecraft and advanced simulations. 

A new frontier of near-Sun plasma measurements is being taken by NASA’s Parker Solar Probe (launched 2018) and ESA’s Solar Orbiter (launching 2020), both flying closer to the Sun than ever before.  These spacecraft fly through the solar wind, hot plasma that continuously streams out from the Sun and populates the interplanetary space.  We will quantify how electron beams resonantly interact with waves in the solar atmosphere and the solar wind.  This will involve measuring the time evolution of the near-relativistic electron distribution function at many different distances from the Sun.  The particle data can then be compared with increases in local electric fields; the signature of beam-generated plasma waves.  The new measurements will allow us to test the validity of existing theories regarding electron energy loss, wave generation, and how both are affected by the turbulent nature of the solar wind.  We will then utilise high-performance numerical simulations that model the transport of electrons and their interaction with plasma waves.  By including our cutting-edge solar wind data measurements as simulations inputs, we will refine our understanding about how electrons lose energy and change trajectories through the solar system. We will also explore the initial acceleration characteristics in the solar atmosphere, where spacecraft are unable to enter.  The new models can make important predictions, including the arrival times of electron beams at Earth.  Such outputs can be used to update space weather models that help secure safety for future space technology.  This combination of simulations and wide-ranging observations presents an exciting opportunity to significant progress human knowledge and understanding of how the Sun works, and how it interacts with our solar system. 

Desired Knowledge and Skills

  • Undergraduate in astrophysics or related field
  • Strong computational skills
  • Experience in data analysis
Chemodynamics of the Milky Way and other Galaxies

Supervisor: Dr Ralph Schoenrich

Galactic astronomy is experiencing a revolutionary increase in available data. A large number of international surveys, like the Gaia satellite mission, is mapping the positions, motions, and properties of our galaxy’s stars as well as external galaxies from ground and space, to reveal the structure, dynamics and history of our own Galaxy and compare it to disc galaxies in general.  The number of stars for which we have good information on position, motion, and surface composition (which tells us where a star came from), has increased by a factor 10^4 or 10^5 compared to what we had 10 years ago. These data can only be fully understood with statistical methods and detailed chemodynamical models. MSSL/UCL has unique competence in both understanding the data from modern surveys and to apply them to constrain e.g. the distribution of dark matter, to understand the detailed structure, e.g. of the Galactic bar and spiral arms, and the history of the stellar populations within the Galaxy's disc and halo. 

Project 1: Astrostatistics and stellar parameters

The progress of modern astronomy is comparable to breakthroughs like the invention of the microscope in biology. Until 10 years ago, the largest spectroscopic samples just comprised a few hundred stars, with spectra being painfully collected and analysed by hand. The modern surveys have given us sample sizes of >10^8 stars with photometry and kinematics, and ~10^7 stars with detailed spectroscopic information. These allow us to determine stellar surface abundances (encoding where a star comes from), surface temperatures and surface gravities (providing stellar ages). However, we cannot just read out these data and draw a picture of our galaxy: uncertainties and biases in these datasets are of a similar size as the signals/differences between populations, and so in a naive analysis most apparent structures will not be real, but be caused by errors in data and models. So, to make sense of these surveys, we need a full analysis of all available information with full assessment of uncertainties and biases.

Our group has developed the first fully integrated Bayesian framework to estimate stellar parameters from stellar spectra, has advanced statistical techniques to control the resulting parameter estimates, in particular for stellar distances, and has experience with machine learning techniques. The PhD student expanding these tools will be able to map stellar populations throughout the Milky Way, and can challenge existing models of stellar atmospheres and stellar evolution. The student can also rely on the group’s expertise to explore the formation and structure of our Milky Way and other galaxies.

Project 2: Chemodynamical modelling of the Milky Way and external galaxies

We want to understand the current structure and evolution of the Milky Way. With such a model, we can also map the dark matter content of our galaxy or probe other models of gravity. In the later stages, we want to compare this knowledge to other galaxies, using surveys of extra-galactic field spectroscopy.

But how can we understand these data? Simply looking at observed motions of stars will not solve the problem, since we need the full distribution of stars in ages, surface abundances, and kinematics, to resolve the observational biases, in particular selection effects, since stars in a survey are not a representative sample of the Milky Way’s stellar population and these biases change with position.

One could now attempt to rely on “full” simulations. However, large simulations (i.e. N-body or N-body + hydrodynamic models) are too costly to run a sufficient number of them for fitting their internal parameters or fitting to the observational data. On the other hand, very simple analytical models or directly analysing data are severely limited by the presence of strong selection biases: the stars ending up in a survey are not a representative sample of the populations in a galaxy, and this selection changes with position.

Our group attempts the middle path, and has already developed comprehensive analytical models that cover both the chemical and structural evolution of a Galaxy. The PhD student can rely on these models, expand them and apply them to the data. In the long run, the model can also be expanded to directly incorporate direct spectroscopic fitting to make sense extragalactic spectroscopic observations, which will be particularly attractive with the advent of the next generation of large telescopes:  So far, the standard way is to extract simple statistical moments (mean, dispersion) for e.g. the stellar velocity distribution in these objects directly from spectra. Directly analysing the data will remove this problem and allow us to directly measure the dynamics and histories of these disc galaxies and to compare them to what we learn from the Milky Way.

Project 3:  Galactic dynamical evolution, mapping dark matter in various ways

The PhD student on this project will tackle stellar kinematics and dynamics to map and constrain the dark matter content in different ways. There are two main (dynamics) strategies to learn about the dark matter content of galaxies like the Milky Way: a) mapping the current distribution of stars in an equilibrium model to find the part of the potential unexplained by visible matter (this path has been described in Project 2), and b) studying the structural evolution of the system to identify and quantify the impact of dark matter. This project employs strategy b). We can identify several such impacts:

  • The dark matter halo influences the spiral patterns of the galaxy and their history can be inferred from the radial migration of stars.
  • The dark halo is responsible for dynamical friction slowing dwarf galaxies in the outer halo, but also the Galactic bar near the centre. Quantifying dynamical friction constrains not only the dark halo density, but also potential degeneracy or velocity distribution of the dark matter.
  • Dark matter influences disk heating: we know that stars today form from the cold gas in a very thin disc in the Galactic mid-plane.  Yet, the stellar populations with increasing age form increasingly thicker discs around this midplane. We have to disentangle: i) heating by spiral structure (influenced partly by dark matter), ii) heating by scattering of stars by Galactic molecular clouds, iii) interactions with lumps in the galactic halo (e.g. accreted systems and subhaloes), and iv) higher turbulence in the past star forming ISM.

This project will develop laws/prescriptions for disc evolution by calculating analytical expectations and comparing them to tailored N-body simulations, e.g. to separate the signatures of galaxy mergers vs. secular heating by spiral arms, bar, and molecular clouds. The student can then impose these laws onto a distribution function model of the Galaxy, by which they can be compared to the observed data. Beyond constraining the dark matter, the project will quantify stellar migration and the merger history of the Milky Way.

Desired Knowledge and Skills

  • Undergraduate in a subject of physics
  • Strong computational skills and/or willingness to learn
  • Good analytical skills


Preparatory Research for the Comet Interceptor Mission

Supervisor: Prof. Geraint Jones

In June 2019, the European Space Agency selected Comet Interceptor as its next planetary mission, to be launched in 2028. MSSL has a leading role in the project, both at mission level and in planned scientific instrumentation to be carried on the spacecraft. Comet Interceptor will be delivered to Sun-Earth Lagrange Point L2, and will stay there awaiting the discovery of a suitable long period comet or interstellar object target. Once one is found, Comet Interceptor will leave L2 to perform a flyby of the object.

The mission, which consists of a main spacecraft and two smaller probes, will carry several different instruments to study the comet nucleus, gas, dust, and its interaction with the solar wind. A PhD studentship is offered to conduct original research in support of the mission’s aims. This research could concentrate on one or more of several topics, including, but not limited to:

  • The search for, and characterization of, suitable long period comets and interstellar objects using ground-based telescopes.
  • A study of how multi-point measurements made by the mission, both in situ and remote, could be employed to learn about the target comet.
  • Designing observational sequences to maximize the scientific return from the mission’s instruments.

The mission’s website is available at www.cometinterceptor.space

Desired Knowledge and Skills

  • Undergraduate degree in astronomy, astrophysics, physics, or another closely-related field.
  • Good computational skills
    Water and dust in the Mars atmosphere from the Rosalind Franklin rover

    Supervisor: Prof. Andrew Coates

    The UCL-led PanCam instrument (Coates et al., 2017) on the ESA-Russia Rosalind Franklin rover is due to reach Mars in 2021 following its planned launch in 2020. The PanCam instrument consists of two wide angle cameras (WACs), each of which include an 11-position filter wheel, complemented by a high-resolution camera. Images from the WACs at different wavelengths will provide geological and atmospheric context during the mission.

    In this project the student will concentrate on the atmospheric science goals of PanCam: water vapour, dust content and variable phenomena. Near sunset on Mars, PanCam will use filters near the 93.6 nm water absorption feature to determine the atmospheric water content along the line of sight. The student will develop models to invert these data into a height profile, and similarly study dust content and dust devils.

    Desired Knowledge and Skills

    • Undergraduate in physics
    • Data analysis skills
    Explosive energy release in space plasmas

    Supervisor: Dr. Colin Forsyth & Dr Mervyn Freeman

    The substorm is a repeatable space weather disturbance, which, like earthquakes, apparently unpredictably releases a considerable and variable amount of energy. This makes the substorm one of the greatest sources of uncertainty in predicting the impact of space weather on electricity supply networks, satellites, and associated services.

    This project aims to improve our understanding of what determines when a substorm will occur by:

    (i) comparing and synthesising diverse catalogues of thousands of substorm onsets from different observational sources and identification methods to reveal their common waiting time distribution and source/method departures from it,

    (ii) examining the common distribution, or departures from it, for evidence of sub-hour recurrence of substorm onsets or of substorm intensifications in so-called compound substorms,

    (iii) testing the common waiting time distribution against that predicted by the Minimal Substorm Model (MSM), and revising the model accordingly,

    (iv) developing the MSM or alternative model to account for any sub-hour recurrence property.

     In the standard model, the substorm evolves through 3 phases over a typical duration of ~3 hours. In the growth phase, lasting ~1 hour, energy from the solar wind is transferred into the Earth’s outer magnetic field environment known as the magnetosphere where it is stored as magnetic energy in the anti-sunward-stretched magnetic lines of force of the magnetotail. Eventually this storage of energy modifies the plasma in the magnetosphere such that a plasma instability occurs at so-called substorm onset. The instability rapidly reconfigures the geometry and topology of the magnetotail magnetic field over an expansion phase lasting ~20-30 min. The reconfiguration releases some or all of the stored magnetic energy into various forms of energy, which are transported and dissipated into the upper atmosphere, the ring current, and into Space in the expansion and subsequent recovery phase lasting 1-2 hrs.

    Consequently, the minimum time between substorms would be expected to be about 3 hours and this can be lengthened by a period when the solar wind input is switched off that effectively interrupts or suspends the growth phase [Freeman and Farrugia, 1995; Freeman et al., 1999; Morley et al., 2009]. This model appears consistent with the statistical distribution of ~1000 substorm waiting times identified from energetic particle data at geostationary orbit [Borovsky et al., 1993]. The distribution has a mode at 2-3 hours, consistent with periodic substorms under continuous driving, and a larger average of 5-6 hours, resulting from a long tail extending beyond 24 h due to the interruptions of solar wind driving. This interpretation has been confirmed by the successful reproduction of the empirical waiting time distribution by the so-called Minimal Substorm Model (MSM) [Freeman and Morley, 2004]. This simple mathematical model is of an integrate-and-fire process in which energy from the solar wind accumulates in the magnetotail until substorm onset occurs at some fixed energy level and then a variable amount of energy is released that is proportional to the solar wind power input at onset. (The MSM also approximately explains the statistical distribution of estimated substorm energy loss into the upper atmosphere [Morley et al., 2007].)

    However, substorms are morphologically complex and thus the identification of onset may depend on observing instrument, location, and identification criteria. Consequently, different substorm catalogues yield generally different substorm waiting time distributions [Forsyth et al., 2015]. Whilst several appear similar to that of the standard model above, some indicate a plethora of waiting times at sub-hour time scales. Observationally, this can be understood as intensifications seen within the standard substorm, creating the so-called compound substorm [e.g., Sandhu et al., 2019]. However, it is unclear whether it is appropriate to view these as intensifications within a substorm or as superposed substorms, which would require revision of the standard model. The distinction is not merely semantic but is important to understanding the physics of the substorm instability, magnitude, and dynamics. It is also vital for operational forecasting.

    In this project, the student will compare and contrast the various substorm catalogues and devise statistical and/or machine learning methods to synthesise the catalogues to yield a robust substorm list and waiting time distribution that is common to all, taking into account observational uncertainties and methodological differences. From this, the student will identify the non-standard component (i.e., sub-hour waiting times) in either the synthesised distribution or more likely in departures from it of individual catalogues. Using this information, the student shall test whether the MSM, or revisions of it, can explain the synthesised distribution and whether and what additional mathematical model is needed to account for the non-standard component. Depending on the outcome and time available, the student will attempt to relate the mathematical model(s) to our understanding of substorm physics with a view to deriving the model mathematics from the MHD equations and approximations of the kinetic instabilities.

    Desired Knowledge and Skills

    • Undergraduate degree in Physics, with a strong interest in solar or space plasma physics
    • Strong computational skills in a relevant programming language (IDL, Matlab, Python)
    • Good statistical and mathematical skills
    X-ray studies of planets in our solar system

    Supervisor: Prof.Graziella Branduardi-Raymont

    The XMM-Newton and Chandra observatories have revealed the beauty and multiplicity of X-ray emissions from planets and comets in our solar system. MSSL has a vibrant research programme in this field which encompasses planetary physics, solar science and the response of solar system objects to the impact of the Sun’s activity. The project on offer focuses on two themes, our own Earth and Jupiter. The fraction of work dedicated to each is flexible and will be decided in discussions with the supervisors.


    The process of charge exchange between ions and neutral particles has been studied since the dawn of atomic physics, but it took until about 20 years ago for it to be recognized as an important contributor to the production of soft X-rays in astrophysical sources. It was first found to be responsible for the bright X-ray emission of comets as they travel near the Sun. In this case we are talking of solar wind charge exchange (SWCX), which is produced in the interaction of highly charged ions of the solar wind with the extended neutral comae of the comets. We now know that SWCX takes place also at Earth when solar wind ions interact with neutral hydrogen in the Earth’s exosphere. The emission’s intensity is proportional to the ion and neutrals densities, so is brightest in the dayside magnetosheath and the magnetospheric cusps. Evidence for this has been provided by XMM-Newton when viewing along lines of sight crossing the terrestrial magnetosphere. MSSL, in collaboration with other institutes in the UK, Europe, Canada, China and the European Space Agency, are developing a space mission called SMILE (www.mssl.ucl.ac.uk/SMILE) dedicated to studying the SWCX emission as a means to reach a better understanding of how the Earth’s magnetosphere responds to the impact of the solar wind. The PhD project involves simulating and investigating the X-ray maps of the magnetosheath and magnetospheric cusps expected to be returned by the Soft X-ray Imager onboard SMILE and also relating them to the UV images of the Northern aurora that SMILE UV Imager will produce.


    Jupiter's polar regions show bright soft X-ray aurorae, with a line-rich spectrum arising from the charge exchange interactions of atmospheric neutrals with local and/or solar wind high charge-state heavy ions, accelerated in the planet’s powerful magnetic environment. At energies above ~3 keV the X-ray spectrum of the Jovian aurora becomes featureless, pointing to an origin from electron bremsstrahlung. Jupiter’s atmosphere also scatters solar X-rays, so that at low latitudes the planet's disk displays an X-ray spectrum that closely resembles that of solar flares. This is a particularly exciting time to investigate Jupiter’s X-ray emissions because of the presence at the planet of the Juno spacecraft which has been orbiting Jupiter since July 2016, making measurement in situ and allowing unprecedented multi-wavelength studies (see an example on the right of Chandra auroral X-rays overposed on the optical image from Juno) as well as conjugate remote and in situ measurements which directly relate to the state of the Jovian magnetosphere. Already an exceptional amount of new knowledge about the physical drivers of the auroral X-ray emission has derived from joint measurements by Juno and remotely by XMM-Newton and Chandra.  A large amount of X-ray data is in hand and available for further in depth investigations and the PhD project will be centred on their analysis and interpretation. 

    Desired Knowledge and Skills

    • Undergraduate in planetary science, astrophysics or physics
    Next generation studies of the Sun’s magnetic activity

    Supervisor: Prof. Lucie Green

    The Sun's character is determined by its dynamic and evolving magnetic field, particularly in regions of intense magnetism known as active regions. When active regions are young they are the source of the most violent and energetic events in the Solar System - coronal mass ejections and solar flares. When active regions die, they disperse their magnetic field across the Sun and remnants of this field become the input for the next solar cycle. Understanding the physical processes that take place in active regions is therefore centrally important to understanding how the Sun operates.
    This project will focus on high-resolution space and ground-based observations of active regions. The work will focus on understanding how the magnetic field of active regions evolves over time and how changes at small spatial scales are able to contribute to the large-scale evolution. The project is particularly timely, as data will soon become available from the DKIST facility in Hawaii. DKIST is a four-meter reflecting telescope with a spatial resolution that reaches the fundamental length scales of photon mean-free path and the plasma pressure scale height, which is needed to study the building blocks of the magnetic field. In the later stages of the project, there will be links to the upcoming Solar Orbiter mission, in which MSSL plays a leading role. Solar Orbiter aims to investigate how changes to the Sun's magnetic field propagate out into the heliosphere, thus taking the project from small-scale magnetism close to the Sun, to large-scale eruptions that modify the near-Earth space environment.


    Desired Knowledge and Skills

    • Undergraduate courses in astrophysics, solar physics or plasma physics
    • Strong computational skills
    How does the solar wind change as it expands?

    Supervisor: Dr Robert Wicks

    The solar wind is the tenuous plasma emitted by the Sun that fills the heliosphere. It is the local astrophysical plasma of the Earth and provides a unique environment to observe naturally occurring plasma generated by a star. There are some big open questions in astrophysical plasmas that we hope to answer by studying the solar wind: how do plasmas that have such low density that they do not have collisions between particles heat up? How do large-scale motions in such collisionless plasmas cause turbulence and dissipate? How does expansion of the plasma away from the star change the small-scale processes in the plasma?

    Parker Solar Probe was launched in August 2018 and has now completed 3 orbits of the Sun, reaching closest approach at only 30 solar radii from the Sun. In February 2020 Solar Orbiter will be launched with a mission to investigate the origins of the solar wind. This project aims to use cutting edge novel observations from these missions to understand and quantify how ions and electrons interact with electromagnetic fluctuations in the plasma to exchange energy and heat, even though there are very few collisions between particles. The student that takes this project will analyse measurements of magnetic and electric fields, proton and electron distributions at different distances from the Sun to explore how expansion changes the properties of the plasma turbulence and dissipation. This will focus on how kinetic particle physics of the plasma channels energy from the turbulence and the global expansion of the solar wind into different modes of heating or cooling.

    The aim of the project is to show that both kinetic plasma physics and global dynamics are essential to explain collisionless plasma behaviour.

    Desired Knowledge and Skills

    • Undergraduate in physics
    • Ability to use data analysis software, e.g. Matlab, IDL, Python, or similar.
    Artificial Intelligence Assisted Dark Universe Science

    Supervisor: Prof. Thomas Kitching

    In June 2022 the European Space Agency Euclid mission will launch. The objectives of the Euclid mission are to make a high-resolution visible wavelength map of the sky to image over three billion galaxies, and to measure near-infrared spectra for several tens of millions of galaxies. By using this data one can create 3D maps of dark matter and determine the properties of dark energy – these dark components account for 95% of the mass-energy content of the Universe and yet their nature is unknown.

    One of the primary ways to determine dark energy properties from Euclid is to measure the shapes of galaxies in order to use a statistical method known as weak lensing. However, the ability to measure galaxy shapes is hampered by the quality of images from the Euclid CCDs. In particular in space CCDs are subject to an effect known as Charge Transfer Inefficiency (CTI) caused by impact of cosmic rays on the Silicon of the detectors. Accounting for and correcting the effect of CTI is required for high quality dark Universe science.

    This project will use Machine Learning, deep neural network, methods to correct images for the effect of CTI. It will involve working closely with the Euclid engineering teams to understand the images and the impact of cosmic rays. It will also involve creating a machine learning model that can correct the images whilst maintaining the integrity of the science that can be inferred from the images. This project will then input any findings into an end-to-end pipeline to assess the impact on the inference of cosmological parameters on the ability to correct for CTI.

    Desired Knowledge and Skills

    • Undergraduate in physics, astrophysics or an associated field
    • Strong computational skills
    Unveiling the diversity and architecture of extrasolar planets

    Supervisor: Dr Vincent Van Eylen

    The possibility of planets orbiting other stars has been a topic of fascination for centuries. We are the first generation that has brought these planets – now known as exoplanets – from the realm of science-fiction into that of science. An important milestone was the discovery of several planets orbiting a pulsar (Wolszczan & Frail, 1992), followed by the first planet orbiting a star more similar to our Sun (Mayor & Queloz, 1995), an achievement recently awarded the Nobel Prize in Physics. The 25 years since have been filled with an abundance of exciting discoveries and today we know over 4000 exoplanets. These planets exhibit an incredible diversity of properties. Why do so many planets have tiny orbits – often much smaller than that of Mercury? What causes planets to become rocky, gaseous, or something in between? Why do some planets have orbits that are strongly eccentric, or misaligned with the rotation of their host stars? What happens to planets when stars evolve away from the main sequence? Which planets are the most favourable and interesting targets for studies of their atmospheres? How unique is our solar system – are we alone?

    Exoplanet science is a young field of research and there is great potential for many ground-breaking new discoveries! Two exciting PhD projects related to these questions are available:

    1. The first project will make use of novel data from the NASA Transiting Exoplanet Survey Satellite (TESS). TESS is the first space mission to provide nearly all-sky photometry, covering an area 400 times larger than that of the previous NASA planet finding-mission, called Kepler (Borucki et al. 2010). The TESS primary mission will be completed by the summer of 2020. During this PhD project we will use these state-of-the-art observations to detect new transiting exoplanets and quantify their occurrence. In particular,  we will investigate the properties of exoplanetary systems around stars of all types and evolutionary properties. Comparing exoplanets around stars with different masses and ages will provide key insight into how planetary systems form, and how they evolve over time. In this way, we will gain fundamental new insights into planet formation theories.

    2. Despite the discovery of thousands of new planets over the last years, much remains unknown about their characteristics. A powerful way of learning about exoplanets is by combining information from different detection methods, in particular from transit surveys such as the NASA Kepler and TESS missions, and from radial velocity (RV) observations from state-of-the-art instruments on telescopes around the world such as the ESO Very Large Telescope in Chile. By combining these sources of data, both sizes and masses of planets can be measured and their composition can be inferred. In this way, we can classify the architecture and diversity of planetary systems. A motivated PhD student will join an international team of experts focused on conducting and analysing RV observations of newly discovered transiting exoplanets. The project holds tremendous potential for a range of exciting new discoveries. We will seek to establish which planets have thick atmospheres and which have no atmospheres, and investigate the reasons for these differences. The student will tap into a large international network of experts to do this challenging but exciting work.

    In both projects a motivated student will sharpen their analytical background and physical knowledge, while developing strong data science skills that will be valuable both in an academic career and outside of academia. Furthermore, there will be ample opportunity to travel to other universities and present new findings in international conferences, as well as conduct observations at telescopes around the world.

    Desired Knowledge and Skills

    • Undergraduate in astrophysics, planetary science or related degree.
    • A background in physics and/or data science is helpful but lack thereof can be overcome with strong motivation.
    • Excellent writing and presentation skills are a bonus, as is evidence of motivation, leadership and creativity
    Observables from extreme mass ratio inspirals in the post-LIGO/Virgo era

    Supervisor: Prof. Silvia Zane

    The first detection of gravitational waves (GWs) by the LIGO and Virgo collaborations in 2015 and 2016 have heralded a new era in multi-messenger astrophysics. However, such detections are limited by technological constraints to higher-frequencies of gravitational waves, corresponding to smaller-mass compact binaries with reasonably small orbits. The ESA-NASA Laser Interferometer Space Antenna (LISA) observatory will be the successor to LIGO and Virgo, a space-based interferometric triangle of detectors capable of detecting hitherto inaccessible regions of the GW frequency domain, such as ultra-compact binaries, supermassive black-hole (SMBH) binaries, and extreme mass ratio inspirals (EMRIs).

    The systems producing GWs and their electromagnetic (EM) counterparts are highly dynamical, and require solving Einstein’s field equations (EFEs) in the time-dependent and nonlinear regime. Proper modelling of such systems requires simultaneously solving the EFEs for the background spacetime (e.g., compact binaries comprising BH, neutron star, white dwarf) along with the general-relativistic magnetohydrodynamical (GRMHD) equations of the accretion flow, and is highly non-trivial.

    This project will begin by using GRMHD simulations of a single BH, modelling the effects of tidal disruption events (TDEs) on the observed emission properties, extracting meaningful observational signatures. With this foundation in place the next step will be to investigate the orbit of a low-mass object (e.g., a stellar-mass compact object) around a massive object (e.g., a SMBH) – due to their high mass ratio these binary systems are known as EMRIs. Such EMRI events offer the potential to place stringent constraints on the properties of the central SMBH.

    This is a theoretical-numerical project, requiring a strong numerical & computational background, as well as necessitating physical/phenomenological modelling and interpretation of synthetic observables.

    Desired Knowledge and Skills

    • Undergraduate in physics/astrophysics.
    • Strong computational and mathematical skills.
    Large scale interactions and plasma processes at solar objects

    Supervisor: Prof. Andrew Coates

    The plasma interactions of the different bodies in the solar system differ, but some processes are key at many of them. The main controlling factors are (1) upstream plasma conditions (including Mach number, dynamic pressure) and (2) the nature of the object (including magnetic field, presence of an atmosphere/exosphere, presence of internal sources and plasma discs, ion pickup processes). The processes involve magnetic reconnection, shocks and ion pickup. In this project available spacecraft data will be analysed including size and morphology, and compared with expectations from theory and modelling to find the importance of the different plasma processes at each.

    Desired Knowledge and Skills

    • Undergraduate in physics
    • Data analysis skills
    Learning to image: AI for imaging and uncertainty quantification in high-dimensional image reconstruction problems

    Supervisor: Dr Jason McEwen

    In many fields high-dimensional inverse imaging problems are encountered. For example, imaging the raw data acquired by radio interferometric (RI) telescopes involves solving an ill-posed inverse problem to recover an image of the sky from noisy and incomplete Fourier measurements. Future telescopes, such as the Square Kilometre Array (SKA), will usher in a new big-data era for radio interferometry, with data rates comparable to world-wide internet traffic today. The recently proposed SPIDER optical interferometric sensor could revolutionalise space observations, due to low weight, low power consumption, and high resolution, but again involves solving an ill-posed inverse problem. The SPIDER concept could also be useful for sensing the surrounding world, e.g. with panels built into autonomous vehicles. Magnetic resonance (MR) imaging (MRI) involves solving a very similar inverse problem to interferometric imaging. MRI also encounters a growing big-data problem as new imaging modalities are considered (e.g. 3D and diffusion MRI) and are pushed to higher resolutions.

    Artificial intelligence (AI) techniques are typically applied to regression and classification problems. While imaging can be considered a type of regression problem, imaging on the whole presents a different type of modality than standard regression problems. Applying machine learning to imaging, rather than standard classification or regression, is a relatively new and immature field, although much progress has been made in recent years. In general when learning to image, “fully-learned” approaches that do not exploit any knowledge of the physics of the problem are difficult since the parameterisation of arbitrary inverse operators must inevitably be very high dimensional. Instead we will take a “knowledge- driven” approach, exploiting the physics of the problem that is encoded in the measurement operator in a deep neural network setting. We will consider network architectures consisting of convolutional and deconvolutional layers. We will develop stochastic models of permissible measurement operators for training to address the issue that the measurement operator may changes with each observation. In additional, we will explore the use of “learnt iterative” schemes that more tightly integrate the physics of the problem with the learning algorithm. We will also investigate the inclusion of uncertainty quantification (e.g. error estimation) into learning to image frameworks, which is an important missing component in many imaging problems, extending recent developments by McEwen to the deep learning setting.

    Spacecraft charging of CubeSats and the impact of space weather events

    Supervisor: Prof. Dhiren Kataria and Dr Anasuya Aruliah

    The project is mainly focused on spacecraft charging study of spacecraft in general and spacecraft in the CubeSat form factor in LEO in particular. The project will develop a model to enable estimations of spacecraft potential and then use the data from the INMS instrument on the CIRCE mission to verify the model. The project would also involve hands on work with the INMS, involvement with all aspects of INMS ground segment and in-flight operations, data analysis of in-fight data and impact of space weather events. 
    Desired Knowledge and Skills

    • Undergraduate or Masters in Physics. 
    • Strong computational skills.
    • Strong interest in hands-on hardware