Galaxies, Active Galactic Nuclei and Cosmology

Supervisory team: Kristen Coppin and Jim Geach

Over half of the star formation energy generation in the Universe is extincted at optical wavelengths and enshrouded by dust which absorbs and re-radiates the starlight in the far-infrared/submm; and the sub-mm and mm atmospheric windows allow us to access the redshifted far-infrared emission from this obscured or ``hidden’’ side of galaxy formation and evolution.  The James Clerk Maxwell Telescope (JCMT) Cosmology Legacy Survey (S2CLS; Geach et al. 2017) and its extension via the SCUBA-2 COSMOS survey (S2COSMOS; Simpson et al. 2017) and now S2XLS (PI Geach) and STUDIES (Wang et al. 2017) are the largest and most sensitive and ambitious single-dish surveys at 850 and 450 micron (in the submm wavebands) ever conducted.  In addition, the 50-m Large Millimeter Telescope in Mexico will be conducting unique and transformative imaging of the sky at millimeter wavelengths through a series of public Legacy Surveys (Ultra-Deep and Large Scale Structure surveys in particular) starting in late 2022 using the new TolTEC camera.  These unprecedented legacy surveys have been yielding thousands of high-redshift galaxies selected in the sub-mm/mm wavebands - providing an order-of-magnitude improvement in the sample sizes of previous surveys at these wavelengths!

With so much data now in-hand there are several possible projects that could be carved out using a combination of these legacy (sub)mm surveys with existing ancillary multi-wavelength data to make progress on a key outstanding question in galaxy evolution: How are dust and metals built up in massive galaxies over cosmic time? Some key science that could be explored with these data sets by a keen student include (for example): 1) Measuring the morphologies and Spectra Energy Distributions of the dustiest (sub)mm galaxies identified in the surveys above with existing public JWST CEERS data; 2) locating and probing the high-redshift tail of the distribution of (sub)mm galaxies (via new mm observations with ToLTEC); and 3) exploring new parameter space on the dust content, obscuration fraction, and gas content in galaxies (via (sub)mm observations) as a function of mass out to much higher-redshift than has previously been explored.  The project can be tailored to some degree to match the student’s interests and skill set and available data.

We are also involved in ongoing efforts to perform detailed follow-up of these high-z submm-detected sources at higher resolution with the Atacama Large Millimetre Array (ALMA) situated at 5000m on the Chajnantor plateau in Chile.  It is envisaged that the findings of this work will feed naturally into new ALMA and other telescope proposals, such as the James Webb Space Telescope (JWST).

Supervisory team: Martin Krause, Darshan Kakkad

Galactic outflows are an important ingredient for understanding the evolution of galaxies, possibly influencing the growth of some galaxies and transporting metals and magnetic fields into the intergalactic medium. One of the main difficulties in diagnosing galactic outflows is to link the driving high-energy plasma, which is typically produced by AGN jets or supernovae, tenuous and hard to observe, to optically well-observable cold clouds, often entrained in outflows. Complex hydrodynamic processes govern the formation, shearing and evaporation of such clouds. This project will use 3D hydrodynamic simulations that are performed by our group and are designed to replicate the turbulent conditions in such outflows and the different gas phases involved. The goal is to determine the physical conditions outflows must have to allow for a given observational signature (mass, temperature, velocities) of the entrained cold clouds. Depending on the interest of the student, the project could move more into the direction of generating their own simulations or more into comparison to available observational data.

Knowledge of hydrodynamics and some prior experience with simulation codes are beneficial, but can also be acquired at the beginning of the project.

Supervisory team: Ashley Spindler, Gulay Gurkan

Disk-type galaxies have wide and varied morphologies, from grand spiral arms and galactic bars, to rings and flocculant disks. The properties of these structures are influenced by the evolutionary history of the galaxy, and in turn influence the develop of their hosts. Bars, for example, drive gas and dust from the galactic disk into the central bulge, triggering intense star formation and feeding the galaxy’s supermassive black hole. But studying the internal properties of these structures across a large sample of galaxies has, until recently, been very difficult. Utilising state-of-the-art AI tools, trained using data from citizen science programs, we can now perform this task at very large scales, producing structural maps of hundreds of thousands of galaxies.

With this data, we can begin to unravel the evolutionary histories of spiral galaxies and answer important questions about their development. Why do some galaxies host bars and others don’t? What causes the shutdown of star formation? How does the pitch angle of spiral arms relate to stellar mass, bulge-to-disk ratio, and other properties? How do ringed galaxies differ from spirals, and are they really the remnants of galactic bars? What is the relationship between disk galaxy structure and active galactic nuclei? There is room in this project to cater to the interests and skill set of the student.

This project will primarily use data from DESI, but opportunities exist to include data from public JWST catalogues such as CEERS and JADES, imaging from the Euclid Space Telescope, and LSST.

Supervisory team: Beatriz Mingo, Bonny Barkus, Martin Hardcastle

Radio galaxies (active galactic nuclei with large-scale jets) play a crucial role in the process we call ‘AGN feedback’, in which actively-accreting, supermassive black holes release some of their accretion energy through the production of radiation-driven winds and collimated, radio-emitting jets. These outflows interact with the surrounding interstellar or intergalactic medium (ISM/ICM), transferring large amounts of energy, which heats up gas and offsets star formation and galaxy cluster cooling.

Large-scale feedback from radio jets in massive host galaxies, mainly responsible for offsetting cluster cooling and slowing down galaxy growth, is now quite well understood. However, recent work at low-frequency radio wavelengths, carried out with the revolutionary LOw-Frequency Array (LOFAR, Shimwell et al. 2019, 2022), has unveiled new populations of radio galaxies that were invisible to older, shallower surveys (Mingo et al. 2019, 2022). Many of these new jets are very different from the ‘traditional’ radio galaxies in massive elliptical hosts: they inhabit galaxies similar to our Milky way in terms of mass and star-formation rate, and although not as powerful or large as some of their well-known counterparts, they still have the potential to significantly influence the evolution of their hosts and large-scale environments (Mingo et al. 2012, Webster et al. 2021a, b). We need to understand the life cycles and energetic output of these sources to determine what role they have played in shaping the evolution of galaxies like our own, and what their large-scale energy contribution has been throughout cosmic history. These results will help shape the next generation of cosmological and MHD galaxy simulations and radio/optical surveys (Square Kilometre Array, Vera Rubin/LSST).

In this project you will study AGN feedback both at high-resolution and as part of a large population study. You will use radio data from LOFAR and optical IFU data from WEAVE-LOFAR to characterise the sources and chart the cycle of black hole fuelling and feedback. You will develop a broad range of data analysis, physics, and statistical skills, become part of the LOFAR Surveys pan-European collaboration network, and have the opportunity to work with UH experts on simulations, galaxy evolution, and machine learning.

Supervisory team: Rob Yates, Chiaki Kobayashi

One of the most fundamental questions we can ask is, “when and where does life form in the Universe?” This question will be investigated observationally by upcoming planet-hunting missions such as ARIEL, PLATO, and the Habitable Worlds Observatory (HWO). But such observational missions can only probe the local neighbourhood around our Sun, giving information on only one small region within our Milky Way galaxy. What about the billions of other galaxies out there, and what about the previous billions of years of cosmic time? To tackle this broader picture, large-scale computational simulations of galaxy evolution are crucial.

In this project, you will play a major role in this endeavour by implementing a model for molecule formation into the large-scale galaxy evolution simulation, L-GALAXIES (Yates et al. 2024). This simulation already contains models for (a) chemical element formation and (b) dust formation. The next step is modelling the formation of complex organic molecules, which can form through bonding of chemical elements on the surfaces of dust grains in the interstellar medium (ISM). These molecules are the fundamental ingredients for life in the Universe.

You will work alongside your supervisors, and collaborators at the University of Leiden (Netherlands), to adapt and implement a neural-network-based emulator for molecule formation into L-GALAXIES. The results will be compared to current CO, H₂O, and organic molecule observations in galaxies from the ALMA and JWST telescopes, and provide crucial predictions for future observational surveys. There is also the possibility of applying the molecule formation model to high-resolution hydrodynamical simulations. Ultimately, this project will allow you to develop your skills in data analysis, coding, and astrophysics theory, to predict molecule abundances in simulated galaxies across all time and space, providing an unprecedentedly detailed overview of when and where life could form in the Universe.

Previous experience with coding in python and C (or C++) is desirable. Previous experience with astrophysics simulations would be useful.

Supervisory team: Sugata Kaviraj, Aaron Watkins, Darshan Kakkad

Our current understanding of the Universe is dominated by bright objects (e.g. massive galaxies like the Milky Way), because such systems are brighter than the detection thresholds of past large observational surveys (e.g. the SDSS). However, the majority of stars in the Universe actually reside in the faint or ‘low surface brightness’ regime, i.e. in objects and structures that are much fainter than the detection limits of past surveys. This regime contains all dwarf (low-mass) galaxies which dominate the galaxy number density, making them critical to our understanding of galaxy evolution. It also includes faint tidal debris created by galaxy mergers, which are key to understanding how gravity, the predominant force in the Universe, shapes galaxy evolution over cosmic time. Put simply, a complete understanding of how the Universe evolves is not possible without a detailed comprehension of the low surface brightness regime.

Astrophysics is currently entering a revolutionary era of new surveys, which not only have large areas but are also incredibly deep. In particular, the Legacy Survey of Space and Time (LSST) and the Subaru Strategic Program from the Hyper Suprime-Cam telescope, are poised to transform our understanding of the Universe, by providing images that are more than 100 times deeper than those from previous surveys. These images will enable us to perform detailed studies of the low surface brightness Universe for the first time.

This project will combine state-of-the-art data from these surveys with in-house cosmological simulations (e.g. NewHorizon) and advanced machine-learning techniques we have developed (e.g. Martin et al. 2020), to perform the first statistical studies of the low-surface-brightness Universe. The project will map the properties of dwarf galaxies in unprecedented detail, over at least half the lifetime of the Universe and quantify the role of key processes like galaxy merging in driving star-formation, black-hole growth and morphological transformation in galaxies over cosmic time.

The student will collaborate closely (through visits and conference trips) with colleagues in Paris, Oxford and a worldwide network of scientists within the international LSST project (in which our team members hold several leadership roles). The project will give the student an excellent skillset in astronomical observation, theory and machine-learning that is well-aligned with this new era of Big Data astronomy.

Supervisory team: Rob Yates, Will Roper (Uni Sussex), Peter Scicluna

As our understanding of the Universe becomes more detailed, so do the computational simulations used to model it. This has led to (a) extremely large processing and memory requirements which need supercomputing facilities, (b) increasingly complex modelling with numerous parameters, and (c) extremely large and unwieldy output datasets. The consequence of this is a rising barrier to access for non-experts, particularly observational astrophysicists and students who want to use simulations that are comprehensive but intuitive. Such a disconnect between theoretical and observational research would seriously inhibit our progress in galaxy astrophysics.

To tackle this issue, you will lead the development of a new kind of galaxy evolution simulation, called pyGALAXIES – a modified semi-analytic simulation within the well-established L-GALAXIES family, designed to be easily adaptable and efficient to run. pyGALAXIES is in its early stages currently, but already includes a basic python-based front-end to make parameter selection and I/O easier, and the capability to use ‘merger graphs’, rather than ‘merger trees’, to better describe the structure of the underlying dark matter halo evolution.

Your goal will be to turn this prototype into a fully-fledged galaxy evolution simulation for the future, by focusing on its user-friendliness and novel ways of optimising performance. In addition to developing the astrophysics models, you could work with your supervisors on optimising the front-end and tasking workflow, visualising data using e.g. user dashboards or phylogenetic trees, improving I/O and data accessibility, applying neural-network-based emulators (or similar) to efficiently constrain parameters, and much more. The exact direction we take pyGALAXIES will be up to you!

You will develop a unique set of skills in both astrophysics and computer science during this project, ideal for the future of data intensive research.

Previous experience with coding in python and C (or C++) is desirable. Previous experience with parallel computing or astrophysics simulations would be useful.

Supervisory team: Martin Bourne, Martin Krause and Martin Hardcastle

Supermassive black holes are believed to reside at the centre of most, if not all, high-mass galaxies, where their energetic feedback can significantly impact their environments, reaching out to megaparsec (Mpc) scales. One such mechanism of feedback is the production of relativistic jets, which, along with the lobes they inflate within the hot halos of galaxy groups and clusters, offer compelling evidence of active galactic nucleus (AGN) feedback in action. This feedback is thought to play a pivotal role in shaping its environment across various scales and in regulating the thermodynamics of the intragroup and intracluster medium. Nevertheless, the characteristics of the large-scale environment, such as its dynamic state and magnetic fields—also critically influence the nature and effectiveness of jet feedback. Understanding this complex interplay between AGN jets and their environments is essential for advancing our knowledge of galaxy evolution.

Numerical simulations have been invaluable for studying these processes, offering insights into how AGN feedback influences galaxies, groups, and clusters. However, the vast range of spatial and temporal scales involved presents substantial challenges to modelling all of the involved processes. To date, there is a limited body of simulation work that considers both detailed jet feedback processes and dynamic group/cluster environments in combination, leaving key questions unanswered.

This project aims to address this gap by running a suite of next-generation magnetohydrodynamic (MHD) cosmological zoom-in simulations using the moving-mesh code Arepo. For the first time, these simulations will incorporate high-resolution spin-driven jets within realistic group and cluster environments. This unique approach will allow for a detailed examination of how jets and the lobes they inflate evolve when subjected to group/cluster weather and magnetic fields. Through this investigation, we will gain valuable insights into how jet energy is transferred to the surrounding environment in realistic systems and how this energy shapes the properties of galaxy groups and clusters. The simulations will also enable the creation of detailed mock observations, including X-ray and radio emission, which can be directly compared to existing observations from instruments such as Chandra, XRISM, and LOFAR. Additionally, these mock observations will be used to make predictions and impose constraints on upcoming observational missions, including the Square Kilometre Array (SKA) and Athena.

Supervisory team: Darshan Kakkad, Sugata Kaviraj, Chiaki Kobayashi

Stars in galaxies form from dense, cold molecular gas (T < 100 K). As they evolve and die, they enrich the interstellar medium with chemical elements, which then contribute to new star formation. Supermassive black holes at the centers of massive galaxies are believed to play a crucial role in regulating this star-formation cycle through a process called active galactic nuclei (AGN) feedback. This feedback can manifest as radiation pressure-driven winds or collimated jets of charged particles, driving high-velocity (>1000 km/s) multi-phase outflows on galaxy-wide scales. The primary aim of this doctoral thesis is to investigate the launching mechanisms of these outflows close to supermassive black holes and assess their impact on the star-formation cycle—specifically on molecular gas, star formation, and metallicity.

The student will have access to data from leading ground- and space-based observatories, including the Very Large Telescope and the Atacama Large Millimeter/submillimeter Array in Chile, the Keck Observatory in Hawaii, and space telescopes such as the Hubble Space Telescope and the recently launched James Webb Space Telescope. Many of these data come from integral field spectrographs, which combine imaging and spectroscopy to provide high-resolution morphological and kinematic maps of different gas phases in galaxies, addressing key scientific questions. Observational data will be complemented by simulation predictions to explore different feedback prescriptions applied in simulations. Based on the student's interests and skills, the project will be tailored on low- or high-redshift galaxies and specific gas phases. The student will be supervised by a team of experts in extragalactic astronomy at Hertfordshire.

As part of this project, the student will join an international network and participate in collaborations like the BASS survey, SUPER survey, GOALS team, and 4MOST-Chilean/AGN Galaxy Evolution survey. Skills developed will include big data analysis, data management, programming, report and grant writing, and both verbal and written communication. Previous experience with basic programming is advantageous.

Supervisory team: Chiaki Kobayashi, Souradeep Bhattacharya

Dwarf galaxies are the building blocks of more massive galaxies such as the Milky Way, and also possess the key information on the nature of dark matter. In this project the student will tackle these fundamental problems using the chemical compositions of dwarf galaxies, which are available from on-going and future multi-object spectroscopic surveys such as on Subaru Telescope in Hawaii. For providing theoretical predictions, we have a hydrodynamical simulation code that already includes basic physics such as star formation and chemical enrichment. The student will update the code (written in c) possibly coupling with radiative transfer and run super-high resolution `zoom-in’ simulations on local LINUX cluster and national supercomputer facility (DiRAC).

Supervisory Team: Daniel Smith and Anastasia Ponomareva

This PhD project presents a remarkable opportunity to investigate one of the most profound mysteries in modern astrophysics: the distribution of dark matter within disk galaxies. By conducting a comprehensive analysis of galaxy kinematics through an innovative combination of high-resolution data from the Multi Unit Spectroscopic Explorer (MUSE) and extensive neutral hydrogen observations from the MeerKAT International GHz Tiered Extragalactic Exploration (MIGHTEE) survey, this project will enhance our understanding of the underlying forces that govern galactic dynamics and influence the evolution of galaxies.

MUSE, an integral field spectrograph on the Very Large Telescope (VLT), provides high-resolution observations of ionized gas emission lines in the inner regions of galaxies. This allows us to map the intricate motions of gas near galactic centres with exceptional detail. Complementing this, MIGHTEE offers extensive neutral hydrogen (HI) data, capturing the kinematics of the outer regions of galaxies where dark matter's influence is paramount. By integrating these cutting-edge datasets, the project will construct comprehensive rotational profiles for a sample of galaxies.

In addition to the kinematic data, this project will use a wealth of multi-wavelength images, covering light from the far-ultraviolet to the far-infrared. By combining these images with the detailed spectral data from MUSE, the project will perform advanced analyses to accurately map the distribution of stars within the galaxies. This integrated approach will address the disk-halo degeneracy—a fundamental challenge in astrophysics where it is difficult to distinguish between the gravitational contributions of a galaxy's luminous disk (composed of stars and gas) and its dark matter halo. Both components influence the galaxy's rotation curve, but their effects overlap in such a way that different combinations of disk and halo properties can produce similar rotational velocities. By obtaining precise measurements of both the baryonic (normal matter) and dark matter components, we aim to disentangle their respective influences on the rotation curves.

Ultimately, this project seeks to identify deviations from conventional dark matter models, such as the Navarro–Frenk–White profile, and to explore how these discrepancies correlate with galaxy properties like mass distribution and angular momentum. Unveiling these deviations could provide critical insights into the true nature of dark matter and enhance our understanding of the interplay between dark matter and baryonic matter in galaxy formation and evolution, potentially challenging and refining existing paradigms within the ΛCDM framework.

Supervisory Team: Carolyn Devereux and Alyssa Drake

JWST data has already shown us that galaxies in the early universe can behave differently than galaxies today. Melia (2023) recently studied JWST high redshift galaxies and found that early galaxies were brighter than expected. By assuming that brighter galaxies mean bigger mass, this result questions the accuracy of the 𝛬CDM model. This problem was first observed by Steinhardt et al (2016) and named it the ‘Impossibly Early Galaxy Problem’.

A recent paper (Sun et al 2023) has found that star forming bursts are more common in early galaxies and so could explain the early galaxy problem. Work is required to understand the properties of early galaxies. The aim of this project is to explore high redshift galaxies and find out about galaxy evolution in the early universe and whether it fits the 𝛬CDM model.

This project will use data from JWST and other surveys to explore high redshift galaxies (of which there are more being detected and at higher redshifts) to determine how their properties and formation are different than galaxies today. The project will compare observations and simulations to determine whether there is a discrepancy between the properties of high redshift galaxies with those predicted by the 𝛬CDM model and if not what could explain the differences. This could lead to important insights into our understanding of the universe.

Supervisory Team: Carolyn Devereux and Rob Yates

Dark matter is distributed across the universe in a large scale structure known as the Cosmic Web; a map of voids, sheets, filaments and knots. It influences how the universe evolves and how and where galaxies form. Nguyen et al (2023) has shown that the growth of structure does not match the expansion of the universe as we would expect from the 𝛬CDM model. This result adds to the growing number of anomalies in our measurements of the universe.

This project uses machine learning and visualisation techniques on observational survey data and simulations to explore how large scale structure has grown. It is possible to compare the early dark matter structure, for example, using the Planck gravitational lensing of the CMB, to the late universe dark matter structure, for example, using galaxy weak lensing maps. The analysis of structure growth could lead to insights into recent cosmological tensions (H_o and S8) as well as how dark energy has changed over time. Work by Buncher and Kind (2020) on using machine learning to classify a simulated version of the cosmic web has demonstrated the usefulness of this approach.

With surveys from telescopes such as LOFAR, Euclid and the Vera Rubin Observatory covering large areas of the sky it is a good time to be exploring the large scale structure growth. Understanding the growth of large scale structure, and the formation of the cosmic web, is important to understanding the universe. This project sits within a department that has a large data science and machine learning activity.

Supervisory Team: Martin Bourne, Sophie Koudmani, Martin Krause and Martin Hardcastle

Virtually all massive galaxies harbour supermassive black holes (SMBHs) at their centres, and these galaxies are predicted to undergo multiple major mergers with similarly sized galaxies over their lifetimes. During the merging process, the SMBHs in each galaxy are expected to sink to the galactic centre, where they form a binary and begin to orbit one another before ultimately coalescing. This project aims to advance theoretical predictions for detecting these supermassive black hole binaries (SMBHBs) as they evolve towards merger. Specifically, the project will focus on the unique gravitational wave (GW) emission and possible electromagnetic (EM) counterparts of SMBHBs, assessing how these signatures vary with binary configuration, geometry, and local environment.

The student will perform (magneto)hydrodynamic simulations of SMBHBs with the moving mesh code AREPO. This will include both circular and eccentric inspirals and account for astrophysical factors such as gas dynamics, stellar interactions, and other local environmental influences. For the first time, these simulations will feature comprehensive SMBH feedback modelling, including SMBH winds, jets, and radiation, by tracking the SMBH spin and the state of the SMBH accretion disc. The student will model GW emission from SMBHBs detectable by pulsar timing arrays (PTAs), the Laser Interferometer Space Antenna (LISA) and the Einstein telescope, focusing on source characteristics, waveforms, and rates of detection. The student will also develop tools for synthetic EM observations and investigate potential EM counterparts, predicting expected variability in radio, optical, and X-ray wavelengths based on SMBHB interactions with surrounding gas, accretion processes, and relativistic jet dynamics. This will enable the student to make predictions for radio detections via Very Long Baseline Interferometry (VLBI) techniques, utilising next-generation instruments such as the Square Kilometre Array (SKA) and the Next Generation Very Large Array (ngVLA). Optical variability searches will leverage data from the upcoming Large Synoptic Survey Telescope (LSST).

This work will contribute to the growing field of supermassive multi-messenger astrophysics by providing a comprehensive toolkit of observational signatures for SMBHBs, optimising the identification and characterisation of these elusive systems in upcoming astronomical surveys. The project’s findings will support future detection campaigns and theoretical developments, shedding light on the dynamical evolution of SMBHBs and the merger-driven growth of galaxies throughout cosmic time. As part of this project the student will have the opportunity to join the LISA consortium.

Supervisory Team: William Alston, Dom Walton, Zhu Liu

Accreting black holes are unique sources of some of the most extreme physics in the universe. They are a testbed for understanding general relativity and high-energy physics processes, having implications for all areas of science and space exploration. Key to solving many unanswered questions in contemporary astrophysics are precise measurements of the black hole mass, spin and detailed knowledge of the processes involved in the accretion and ejection of matter around the black hole. The accretion of matter is non-static, so the application of time series methods to the data provides important insight into the geometry and physical processes involved – something which spectra or spatial information cannot achieve alone.

The recent discovery of short time delays – or reverberation - between the intrinsic X-ray emission and the accretion disc reprocessing component around the black hole has made it possible to spatially map the close regions around black holes. This spatial information is measured by modelling the response of the disc to a flash of X-rays coming from the corona. This allows us to decode the geometry, relativistic effects and light bending in the extremely curved spacetime. Recently, we have modelled the reverberation signal in a nearby supermassive black hole as the system evolves over several months (Alston et al 2020). This revealed for the first time a dynamic picture of material around the event horizon, turning our previously static picture of the inner accretion flow into a movie. This powerful method means we are able to resolve structures to an accuracy of ~1 gravitational radii (Rg) - equivalent to measuring structures the size of the sun at a distance of over 1 billion light years.

This project involves the development and application of timing analysis methods, such as Fourier analysis and Gaussian Processes, as well as other data science and machine learning methods, to a wide variety of X-ray and optical data on accreting black holes. Together with the development and application of theoretical models, we will make the most accurate measurements of black hole mass and spin to date in a large sample of sources. The understanding we will gain from sources in the local universe will allow us to push this method to higher redshift and to test models of black hole growth over cosmic time.

Supervisory Team: Daniel Smith, Luke Holden, Marina Arnaudova

Starting in 2025, the WEAVE-LOFAR survey will use the new WEAVE multi-object spectroscopic facility on the William Herschel Telescope to obtain spectroscopy of radio sources identified in the new LOFAR Surveys. The LOFAR surveys have the best combination of huge sensitivity, resolution and sky coverage, and they are already revolutionising our understanding of the Universe. However, WEAVE-LOFAR is the key for taking this to the next level, by providing the most accurate redshifts, source classifications and the most inclusive sampling of the active sources in the Universe. Over the coming five years WEAVE-LOFAR will generate a huge library of optical spectra, and this is the first time that spectroscopy of radio sources has been done on this scale - giving WEAVE-LOFAR access to a huge discovery space. This PhD position is ideally-timed to get your hands on the first data from WEAVE-LOFAR, and join the team writing the first publications. Lots of different directions are possible, including studying star formation evolution in galaxies over the last half of cosmic history, searching out and characterising the most star-forming galaxies in the Universe, or making new measurements of the influence of accretion by black holes on galaxy evolution. The possibilities are virtually endless. If this sounds like something that would interest you - please do get in touch.