School of Science

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Current studentships

Loughborough University is one of the UK’s leading centres of excellence for teaching and research in STEM subjects – with a track record in supplying industry with high calibre, highly motivated graduates, and a vibrant international research culture.

The School of Science brings together the Departments of Chemistry, Computer Science, Mathematical Sciences, Physics and the Mathematics Education Centre, and boasts state-of- the-art facilities and an active research community. We are renowned for the relevance of our research work which is driven by society’s need for solutions to real-life issues. Loughborough scientists are actively engaged in cutting edge theoretical and applied research that is shaping the future and transforming the world through technological advances and scientific discovery.  In the most recent Research Assessment Exercise (RAE) each of the academic departments had at least 85% of their research ranked as ‘internationally recognised’.

These are a few recent research highlights from within the School of Science:

Extensive investment in the student learning environment and research facilities makes Loughborough an excellent place to pursue academic aspirations, research interests and career goals and applications are invited for a number of funded studentships in the School of Science.

As members of the Doctoral College, all postgraduate research students benefit from the full programme of Doctoral College events, such as such as the Annual Research Conference, the Summer Showcase, Three Minute Thesis competitions and regular Café Academique seminar series. Our full programme of activities is designed to enhance research culture, networking skills and promote multi-disciplinary thinking and creativity.

In addition, the Doctoral College offers an extensive training and development programme, mapped to the Vitae Researcher Development Framework. Training sessions include face-to face workshops, online and blended learning. To complement structured learning, students are also encouraged to undertake researcher-led initiatives, peer to peer support, and to apply for competitive funding for skills and CV enhancement. You will have the opportunity to undertake training in teaching skills and use these skills to teach undergraduate students. You will also have access to a dedicated postgraduate research careers consultant, to help you succeed in your research and future career.

Applications are invited for the following funded studentships:

Academic Year 2019-20

Computer Science projects

Distributing algorithms

Supervisor: Dr Lars Nagel (; phone: +44-15-09-222328)

Nature of work: This is mainly a theory project which aims developing and analysing algorithms for networks and distributed systems.

Area: Algorithms / protocols for networks and distributed systems; load balancing, routing, data flow problems; network function virtualisation

Potential implications: Results will help to get a better understanding of common problems in new types of networks and environments. The outcomes may include algorithms or heuristics and complexity results for such problems and networks. This may benefit Big Data processing and define limits for the Internet of Things.

Brief description: This project seeks to investigate optimisation problems in the area of distributed computing and computer networks. With current developments like the Internet of Things, Big Data and network function virtualisation, new types of problems arise which require efficient and fault-tolerant algorithms and network topologies for routing, storing and processing large amounts of data. The aim of the project is to develop and analyse such algorithms and networks for problems like routing, load balancing and the placement of network functions.

We seek candidates with excellent programming skills and a strong passion for theoretical computer science who are have experience in one or more of the following topics: the design and analysis of algorithms; computational complexity; the modelling and simulation of networks; graph theory.

How to apply:

All applications should be made online at Under programme name, select ‘Computer Science’.

Please quote reference number: LN/CO/2020. Deadline 15th March 2020.

Mathematical Sciences projects

Self-Assembly of Colloidal Structures in Living Liquid Crystals

Supervisor: Tyler Shendruk (; tel: +44 (0)1509 223 197

Nature of work: Simulations

Area: Biofluids

Potential implications: Understanding the multiscale material physics of living systems and using that new understanding to design future materials.

Brief description: We are seeking four applied mathematics or biophysics students, who are interested in numerically simulating intrinsically out-of-equilibrium materials. This project will investigate the dynamics of colloids embedded in active fluids, biological fluids that spontaneously flow due to internal energy. Examples of such active fluids include cytoplasmic mixtures of filaments, motor proteins that drive flows and biochemical fuel that powers the spontaneous flows. PhD researchers will investigate whether pairs of colloids form self-assembled dimers that function as a self-propelled rotor and explore whether many small colloids form dynamic steady-state structures around larger colloids. The students’ tasks will include developing numerical algorithms to simulate and explore the self-assembly of such colloidal structures. We are particularly eager to see diverse applicants who demonstrate creativity, and an eagerness to numerically model exciting and dynamic systems at the intersection between biology and materials science.

Funding information: The ERC-funded 3-year studentship provides a tax-free stipend of £15,009 per annum (in line with the standard research council rates) for the duration of the studentship plus tuition fees at the UK/EU rate. Due to funding restrictions, this is only available to those who are eligible to pay UK/EU fees.

How to apply: All applications should be made online at  Under programme name ‘Mathematical Sciences’.

Please quote reference number: TS/MA/2020.

Closing date: 17th January 2020



Foam Electrokinetic Separation


Primary supervisor: Dr Dmitri Tseluiko

Secondary supervisor: Dr Hemaka Bandulasena, Dr Anna Trybala, Professor Andrew Archer

Application details:

Reference number: NAN0/MA/DT-2020

Start date: 1st July 2020

Closing date: 30th April 2020

Project Detail:

Following the establishment of a Centre of Doctoral Training (CDT) in “Designed Self-assembly of Nanoparticles within Fluids and at Interfaces” at Loughborough University, applications are invited for a fully funded PhD studentship. The CDT is a joint initiative of the School of Science (Departments of Mathematics and Chemistry) and the School of School of Aeronautical, Automotive, Chemical and Materials Engineering (Departments of Chemical Engineering and Materials), and it offers a unique opportunity to join a vibrant team of researchers working in a multidisciplinary environment across the fields of mathematical modelling, chemistry, chemical engineering and colloidal science. This project will be based in the Department of Mathematical Sciences and will in close collaboration with the Micro/Nano Engineering Research Group within Chemical Engineering.

The successful candidate will investigate electrokinetic phenomena in foams and free liquid films, with the view of developing a novel separation technique. We will target biological entities such as proteins and cells that are difficult to separate by conventional methods and that are useful in pharmaceutical industry. This novel approach will provide tight control of molecules by manipulating strict conditions required for their effective separation. The research will involve mathematical modelling, numerical simulations and the use of state-of-the-art flow visualization tools.

Find out more:

Entry requirements:

Applicants should have, or expect to achieve, at least a 2:1 Honours degree (or equivalent) in Engineering or Science discipline. An appetite and aptitude for interdisciplinary research is essential, as well as good communication and experimental or computational skills. A relevant Master’s degree and/or experience in these areas will be an advantage.

Funding information:

This studentship will be funded through an EPSRC DTP. This 42-month project will be part of a 5-student cohort, working collaboratively to deliver specific goals. The studentship will be further enhanced by a DTP-specific induction and training.  It consists of a tax-free stipend of £15,009per annum, plus UK/EU tuition fees. Additional funds via a Research Training Support Grant will be available. Due to funding restrictions, this is only available to those who are eligible to pay UK/EU fees. To qualify for a full award, all applicants must meet the EPSRC eligibility criteria including the minimum UK residency requirement:


Contact details:

Name: Dr Dmitri Tseluiko

Email address:

Telephone number: +44 (0)1509 22 3190

How to apply:

All applications should be made online at  Under programme name, select Mathematical Sciences.

Please quote reference number: NAN0/MA/DT-2020.

Academic Year 2020-21

Chemistry projects

Lowering the H2 production cost in Methane Cracking Technology by employing solid carbon as an Energy Storage Material

Supervisor: Professor Upul Wijayantha (; phone: +44-15-09222574)

Co-supervisors: Dr Niladri Banerjee (Loughborough University) and Dr Pritesh Hiralal (Zinergy UK, Cambridge)

Nature of work: The project is experimental.

1)     Reproduction of the current state-of-art hydrogen conversion levels by methane cracking and advanced characterisation of collected solid carbon products. This will lead to further understanding of H2 generation in relation to the carbon growth.

2)     Systematic alteration of process conditions (eg. temperature, catalyst formulation, methane flow rate) to obtain value-added solid carbon without compromising the H2 yield.

3)     Use the value-added carbon in supercapacitor electrodes and evaluate the energy storage properties and benchmarking (in collaboration with Zinergy UK).

Further develop methane cracking technology to obtain solid carbon with textures desired for supercapacitor electrodes (at high H2 yield), by considering the business models developed in School of Business and Economics.

Potential implications: This is an exciting project designed to investigate the low-cost hydrogen generation from methane cracking by adding value for the technology (by employing solid-carbon materials in energy storage applications).

Affordable Hydrogen: By adding vale for methane cracking technology, this project is aimed to make the technology economically competitive with the currently existing thermochemical and electrochemical hydrogen generation methods.

Decarbonisation of Energy Network: The technology will convert methane to clean H2 fuel and capture carbon as a value-added product hence directly contribute to decarbonisation of energy network.

Industrial Engagement: The project will be conducted in close association with the relevant UK industry who work in gas and energy storage area (Zinergy UK, EffecTech). This will provide an ideal opportunity for the candidate to expose to UK industry maximising career prospects after the PhD.

Brief description:

COVID-19 lockdown gave us a unique opportunity to understand real carbon emission levels when transport and industry sectors are closed thereby exploit opportunities to adopt hydrogen and renewables centred energy landscape. The long-term future of current thermochemical and electrolytic methods is questionable due to the high H2 production costs. Alternative flexible methods such as methane cracking are rapidly gathering momentum which converts methane to clean hydrogen and capture carbon as a solid.

This project will investigate systematic alteration of process conditions to obtain value-added solid carbon specifically for energy storage whilst maintaining hydrogen yield at high. It will commence by reproducing the state-of-art hydrogen yield and analysing the solid-carbon by-product to understand the growth process and its dependence on the process parameters. Subsequently, individual process conditions will be studied to obtain a series of solid-carbon products with high H2 yield. The carbon products will be used in supercapacitors. Outcome of the project will be feed into develop new business models (which will be separately conducted in School of Business and Economics). The project will be conducted in collaboration with Zinergy UK where benchmark testing of supercapacitors will be conducted.

How to Apply: All applications should be made online either via Sustainable Hydrogen CDT at or via Loughborough University at For Loughborough University applications, please select Chemistry under program name and quote reference number: SusHy/CDT/2020.


Luminescent Molecular Probes to Facilitate Drug Discovery

Supervisor: Stephen Butler (; phone: 01509 222577)

Nature of work: Experimental Synthetic Chemistry

Area: Organic supramolecular chemistry and chemical biology.

Potential implications: New bioassay technologies for drug discovery and medical imaging

Brief description: Many drugs act by inhibiting enzyme activity. To increase confidence in the selection of lead drug compounds it is crucial that pharmaceutical companies have robust assays to measure enzyme activity accurately. The aim of this PhD project is to develop molecular probes that bind reversibly to specific biological anions (e.g. ADP), which are produced during pharmaceutically important enzyme reactions (see Figure). Upon binding to the anion, the probe will provide a luminescent signal that enables precise measurement of enzyme activity in real-time. This project will provide a vital step towards the accurate determination of enzyme kinetics, enabling better selection and validation of new drug candidates at an early stage in drug discovery, reducing effort pursuing compounds destined to fail at the more lengthy and costly phases of drug development. This project spans the areas of synthetic organic, supramolecular and biological chemistry. The student will gain excellent training in organic synthesis, study of host-guest interactions, bioassay development and cellular imaging. They will have the opportunity to work in the laboratories of collaborators (in the UK and France), to test the molecular probes in biological assays.

How to apply:

Applications should be made online at Under programme name, select Chemistry.

Quote reference number: SJB/CM/2020

Closing date is 14th February 2020.

Computer Science projects

Automatic DeepFakes Generation and Identification

Supervisor: Haibin Cai (; phone+44(0)1509223157).

Nature of work: Evolves development of deep learning algorithms

Area: Computer Science

Potential implications: The outcomes of this project can be used to generate video data for entertainment and identify fraud video information


Brief description:

The fake videos generated by Artificial Intelligence (AI) technologies, known as DeepFakes, have come to a stage where it is hard for a human to distinguish them. They can be generated easily using recently developed tools like FaceSwap, Face2Face, and DeepFaceLab. The subject in the fake videos can appear to do some activities that do not happen, thus might cause serious consequences. The spread of these manipulated videos over the internet is a huge danger in damaging businesses, crippling reputations, misleading elections, etc. Thus, it is important to develop advanced deep learning technologies to fight against synthetically generated fake information.

The purpose of this project is to prevent the spread of these misleading fake videos by developing effective Deepfake detection algorithms.  This will involve the automatic generation of high-quality synthetic videos and the identification of these videos. Different deep learning models like Convolutional Neural Networks, Generative Adversarial Networks, and Recurrent Neural Network will be explored to improve the generation and identification performance.

The successful candidate will have the unique opportunity to work collaboratively with the Loughborough AI research team in Computer Science for three years.  The research will also provide opportunities to attend academic conferences, summer schools, and other training courses to improve technical skills. You will master advanced deep learning techniques and have excellent career prospects on the successful completion of the PhD.

How to apply:

All applications should be made online at  Under programme name, select Computer Science.

Please quote reference number:  HC/CO/2020.

The closing date is 14th February 2020.

Knowledge Driven Search-Based Optimisation for Software Performance

Supervisor: Tao Chen (; phone+44-1509 564 118).

Nature of work: Experiment

Area: Software Engineering; Artificial Intelligence; Computational Intelligence

Potential implications: Investigating novel approach to intelligently optimize the performance of software that can potentially affect millions in the world.

Brief description:

The performance of software system is a crucial, but challenging property to be maintain due to the complexity in code, configuration and architecture of the software. This project seeks to rely on Search-Based Software Engineering, a paradigm that concerns with tailoring AI/search algorithms for various software engineering problems, to intelligently optimize software performance at various levels and granularities. The student will investigate how to specialise and design novel search algorithms, such that the expertise knowledge of software engineers can be combined, in order to find better performance results quicker. The student will evaluate the approach using both experimental and empirical evaluation, under real-world open-source software systems and/or datasets that are widely used. Students with strong background on Software Engineering, Artificial Intelligence, Computational Intelligence, Operations Research and their intersection are a plus.

How to apply:

All applications should be made online at  Under programme name, select Computer Science

Please quote reference number: TC/CO/2020.

The closing date is 14th February 2020.

Towards to the interpretable medical imaging analysis

Supervisor: Hui Fang (; phone+44-15-09-222579); Gerald Schaefer (; phone+44-15-09-635707)

Nature of work: Algorithm design.

Area: Medical imaging, Artificial Intelligence.

Potential implications: Applications

Brief description:

The development of artificial intelligence (AI) applications has attracted significant investments in a variety of fields, such as digital healthcare, autonomous driving, precision agriculture and the financial sector. Although many of these applications have achieved impressive outcomes, the majority of tools in serious applications require a higher level of interpretability. This is particularly true in healthcare. For example, it is necessary for clinicians to have sufficient confidence when using AI in screening programmes or to support diagnosis. In this PhD project, the student will develop powerful medical image analysis algorithms, based on recent AI paradigms such as deep learning and attention-based learning, with a focus on interpretability and incorporation of domain-specific knowledge. Potential medical applications include skin lesion analysis for melanoma identification, retinal image analysis, brain lesion analysis, and digital pathology.

How to apply:

All applications should be made online at  Under programme name, select Computer Science.

Please quote reference number: HF/CO/2020.  Closing date is 14th February 2020.

Deep Reinforcement Learning for Human-Robot Interaction

Supervisor: Professor Qinggang Meng (; phone+44-1509635676)

Nature of work: This project consists of theoretical studies, and evaluation on both simulation and robot experiments.

Area: AI, deep learning, intelligent robotics, human-robot interaction.

Potential implications:  This research has applications in service robotics, collaborative robot in manufacturing, and human-robot interaction in general.

Brief description: Short summary (~150 words), add a picture/image to attract students.

Human-robot interaction plays an important role in service robots and collaborative robots (Cobot) in manufacturing. Based on prior knowledge and sensory information, human can easily understand the other person's movement intention during social or physical interaction.  But this is a very challenging task for a robot even with the latest technology in AI and robotics. This project will develop a novel approach to learning such human-robot interaction skills based on the latest deep learning and reinforcement learning algorithms. It will investigate algorithms for some fundamental tasks in human-robot interaction such as human intention understanding, joint attention recognition and prediction of human movement trajectory. The developed approaches will be evaluated in simulation and also by using a real robot like a Pepper robot.

How to apply:

All applications should be made online at  Under programme name, select Computer Science.

Please quote reference number: QM/CO/2020.  Closing date is 14th February 2020.

Mathematical Sciences projects heading

Birational Geometry of Singular Fano Varieties

Supervisor: Dr Hamid Ahmadinezhad (; phone+44-15-09-222628).

Nature of work: Theory

Area: Algebraic Geometry.

Potential implications: Pure mathematics.

Brief description: Short summary (~150 words), add a picture/image to attract students.

The project is about geometric objects called “Fano varieties”. The study of Fano varieties appears in many contexts in pure mathematics, with applications in Geometric Design, Theoretical Physics and beyond.

Geometric shapes defined by algebraic equations are called “varieties”. Familiar examples are the parabola (y=x2) and the sphere (x2+y2+z2=1). Sometimes two varieties can be identified after removing small subsets from them. For example, a sphere without its north pole can be projected to the 2-dimensional plane by a technique called stereographic projection. Identifications like this are called “birational maps”.

Knowing which varieties are birationally mapped into each other is a fundamental question in geometry. The main aim of this project is to study birational relations amongst Fano varieties that may have singular points.

How to apply:

All applications should be made online at  Under programme name, select Mathematical Sciences.

Please quote reference number: [Niladri B1]  HA/MA/2020.  Closing date is 14thFebruary 2020.

Schrödinger operators with complex potentials

Supervisor: Jean-Claude Cuenin (; phone+44-15-09-223243).

Area: Analysis / Mathematical Physics

Brief description: The Schrödinger equation describes the motion of quantum mechanical particles. The spatial part of the equation is governed by a so-called Schrödinger operator, a partial differential operator that can be viewed as a quantization of the classical Hamiltonian. The eigenvalues (or more generally, the spectrum) of this operator are the possible energies of the system. Conservation of energy requires that the Schrödinger operator is self-adjoint (“symmetric/hermitian”); in particular, the potential must be real-valued in this case. However, many interesting physical phenomena (resonances, dissipation of energy etc.) are modelled by Schrödinger operators with complex-valued potentials. Mathematically, complex potentials pose a significant challenge, and the theory is much less developed than its classical counterpart dealing only with real-valued potentials. Many interesting open problems can be found here: Even though rapid progress has been made in recent years there is a need for more (counter)-examples.

The aim of this project is to construct explicit (counter)-examples of complex potentials leading to spectral behaviour that is “unexpected” from the point of view of the classical theory. The candidate will also have the opportunity to participate in workshops of our LMS Joint Research Group “Challenges in Non-Self-Adjoint Spectral Theory” (see

How to apply:

All applications should be made online at under program name Mathematical Sciences.

Please quote reference number: JC/MA/2020.  Closing date: 14th February 2020.

Random Periodicity in Dynamics with Uncertainty: Ergodic Theory

Supervisors: Professor Huaizhong Zhao (; phone+44-1509-222876), Dr Chunrong Feng (; phone+44-1509-223969)

Nature of work: Theory.

Area: Stochastic analysis, mathematics.

Potential implications: Successful completion of a PhD in this area will give a solid basis for a career in academia or industry.

Brief description:

The overall aim of the research is to create a random periodic model, to build a theory of periodic stochastic dynamics, and to design a dynamics model under non-additive expecta-tions. This particular project is part of a programme to establish periodic measures and qua-si-periodic measures of physical relevant stochastic differential equations and stochastic par-tial differential equations. It lies at the intersection of stochastic analysis, dynamical systems and nonlinear partial differential equations. This candidate will pursue cutting edge research towards a PhD degree in stochastic analysis associated with the recent 5-year EPSRC Estab-lished Career Fellowship of Professor Huaizhong Zhao. This is an excellent opportunity to be working with a team of supervisors of world leading experts.

How to apply:

All applications should be made online at  Under programme name, select Mathematical Sciences.

Please quote reference number: HZ/MA/2020.  Closing date is 14th February 2020.

Physics projects

Manipulating magnetic anisotropy with superconductivity

Supervisor: Dr Niladri Banerjee (; phone+44-15-09-222596).

Nature of work: Experimental.

Area: Magnetism, superconductivity, thin films and nano-fabrication.

Potential implications: The project is mainly in the area of thin film deposition, fabrication of devices and measurements of superconductor/ferromagnet heterostructures. It is aligned with the energy area.

Brief description: Recent experiments have identified a unique form of superconductivity formed of equal-spin Cooper pairs instead of the conventional anti-parallel spin pairing [1]. This superconductivity arises at superconductor/ferromagnet thin film heterostructures with complex magnetic textures. Last year, we discovered a fundamentally different route to generate this unique equal-spin superconductivity: using the subtle relativistic spin-orbit coupling instead of complex magnetic textures [2]. In addition to simplifying the structures to generate equal-spin superconductivity, it opens the striking possibility of discovering novel effects in these thin film heterostructures where superconductivity, ferromagnetism and spin-orbit coupling coexists.

In this project, the student will explore one such effect which we theoretically predicted recently [3]: modifying the magnetic anisotropy and thereby enabling reorientation of a magnetic moment purely driven by superconductivity. A successful demonstration of this effect will establish reorienting (or switching) a magnetic moment without applying an external magnetic field and could provide the framework to design future ultra-high capacity cryogenic magnetic memories.

The project will involve thin film deposition of spin-orbit coupled heterostructures and superconductors using sputtering and pulsed laser deposition techniques. Standard low-temperature electron transport and magnetic measurements will be performed in addition to structural characterisation like x-ray diffraction and electron microscopy. The student will also fabricate nanoscale devices using nano-fabrication techniques in the newly built cleanroom at Loughborough.

The student will be working in a diverse group of PhDs, postdocs and research assistants and will also be associated with the Centre for the Science of Materials, Loughborough which brings together experts from several departments to address advanced problems in materials science.

[1] N. Banerjee, A cool spin on supercomputers, Physics World, Volume 32, Number 4, 2019.

[2] N. Banerjee et al. Controlling the superconducting transition by spin-orbit coupling, Phys. Rev. B. 97, 184521, 2018.

[3] L. G. Johnsen, Magnetization reorientation due to the superconducting transition in heavy-metal heterostructures, Phys. Rev. B. 99, 134516, 2019.

Loughborough University has a flexible working and maternity/parental leave policy ( and is a Stonewall Diversity Champion providing a supportive and inclusive environment for the LGBT+ community. The University is also a member of the Race Equality Charter which aims to improve the representation, progression and success of minority ethnic staff and students. The School of Science which includes the Department of Physics, is a recipient of the Athena SWAN bronze award for gender equality.

How to apply:

All applications should be made online at  Under programme name, select Physics.

Please quote reference number: NB/PH/2020. 

Closing date is 14th February 2020.

Fabrication and characterisation of memristors for object detection applications

Supervisor: Dr Pavel Borisov (; phone+44-15-09-228260).

Nature of work: experiment.

Area: Thin film oxide materials and devices.

Potential implications: Artificial intelligence processors, sensors.

Brief description: The mammal brain still outperforms a standard desktop computer, whether by lower error rates or by higher energy-efficiency, when it comes to image and speech recognition, object detection and tracking. Designing electrical circuits that mimic operation of a biological neural tissue is a possible solution. A suitable basic building block is the device of memristor that can be formed by thin films of solid-state oxides. Memristor’s electrical resistance is variable and dependent on the amount of charge that was conducted through them. This project is a collaboration between Physics and Chemistry and its goal is experimental development of memristors that demonstrate resistive switching on short-term timescales. You will employ physical and chemical vapour deposition techniques and other suitable wet-chemistry techniques in order to prepare thin films of oxide materials; study structural, physical and electrochemical properties by a range of techniques, for example, X-ray diffraction and X-ray reflectometry, scanning electron microscopy, X-ray photoelectron spectroscopy, impedance spectroscopy and many others.

How to apply:

Applications should be made online at  Under programme name, select Physics.

Please quote reference number: PB/PH/2020.  Closing date is 14th February 2020.

Intraoperative parathyroid imaging

Supervisor: Sarah Bugby (; phone+44-15-09-222 809).

Nature of work: This PhD will feature a mix of Monte Carlo simulation, design and development of a new imaging system, and experimental testing and analysis. There is the potential to be involved in preclinical or clinical trials (dependent on the success of external funding applications).

Area: Interdisciplinary. Physics. Medical imaging. Nuclear medicine. Compound semiconductors. Technology development.

Potential implications: Parathyroid surgery is extremely challenging. This project will develop an imaging system to localise the parathyroid during surgery using dual-isotope imaging with the goal of improving patient outcomes for this procedure. Wider applications in medical imaging may be investigated over the course of this project.

Brief description:

The parathyroids are small glands located in the neck and, in patients where they are working incorrectly, the most comment treatment is surgery. As the parathyroids are small and visually appear very similar to the surrounding tissue, nuclear medicine scans are taken prior to surgery. These can identify the parathyroids by looking at the uptake ratio of two different radiopharmaceuticals – based on 99mTc and 123I. Although this helps with surgical planning it is not ideal, as anatomical landmarks will shift with patient position and during the course of surgery. Currently, radioguidance during surgery is performed with non-imaging probes. Although a small number of intraoperative imaging systems are available, these are not widely used and have not been designed with dual-isotope imaging in mind.

This project will investigate the use of a compound semiconductor detector system to perform dual-isotope imaging. The existing system is compact and has been shown to have excellent energy resolution but has never been tested for this specific application. This project will first require the creation of an accurate computer simulation based on experimental detector performance and example patient data. This simulation can then be used to investigate a range of designs for the imaging system, and once the optimum design has been chosen a prototype device will be built.  The prototype will be tested with anatomical models in the lab, and potentially through preclinical or clinical trials.

This project is offered in the Centre for Sensing and Imaging Science, an interdisciplinary centre in the School of Science, Loughborough University. During the project you will work closely with Dr Saba Balasubramanian – Consultant Endocrinologist at Royal Hallamshire Hospital. You will also be joining a wider collaboration with members at the Rutherford Appleton Laboratory and Royal Surrey County Hospital.

How to apply:

All applications should be made online at  Under programme name, select Physics.

Please quote reference number: SB/PH/2020.  Closing date 14th February 2020.

Signatures of spin liquid behaviour in correlated quantum magnets

Supervisor: Ioannis Rousochatzakis (; phone+44-15-09-223303).

Nature of work: Computational.

Area: Quantum many body spin systems

Potential implications: The aim of the project is to make rapid headway in diagnosing spin liquid behaviour in existing materials, which is currently the central challenge in the field. This progress is indispensable for the future exploitation of these materials in quantum technologies and computing applications. The prospect of typicality and thermalisation working down to low temperatures in such systems can revolutionise the way we model correlated materials in the computer.

Brief description: According to Boltzmann statistics, matter tends to maximise its entropy at high temperatures and minimize its energy at low temperatures. A ferromagnet (Fig 1a) offers the simplest scenario for this battle between energy and entropy [1]: Below a characteristic temperature TC, the underlying array of spins minimize their mutual energy by ordering along a common direction (Fig 1b), whereas above TC spins fluctuate constantly in random directions (Fig 1c). The spin liquid materials discovered in recent years offer an alternative and much richer scenario, as their energy minimum can be achieved in an infinite number of ways (Fig 2). At low temperatures, such systems therefore sample a macroscopic number of competing low energy states and can thus evade ordering, opening the door for more exotic phases with long-range entanglement and fractionalized excitations [2,3]. This project will explore the experimental signatures of spin liquids by testing the typicality hypothesis [4] in a number of realistic models, using numerical exact diagonalization techniques.


[1] E. Ising, Z. Phys. 31, 253 (1925); L. Onsager, Phys. Rev. 65, 117 (1944).

[2] L. Balents, Nature 464, 199-208 (2010).

[3] I. Rousochatzakis, Y. Sizyuk, N. B. Perkins, Nat. Commun. 9, 1575 (2018).

[4] I. Rousochatzakis, S. Kourtis, J. Knolle, R. Moessner, N. B. Perkins, PRB 100, 045117 (2019).

How to apply:

All applications should be made online at  Under programme name, select Physics.  Please quote reference number: IR/PH/2020.  Closing date is 14th February 2020.

Brain-inspired computers: from neuron spike dynamics to spike computing

Supervisor: Sergey Saveliev (; phone: +44-15-09-22-33-02).

Nature of work: This work involves simulations and theoretical studies to analyse experimentally available data.

Area: Physics and Computer science: physical principles of neuromorphic artificial intelligence.

Potential implications: Design of smart devices for autonomous and mobile intelligent systems.

Brief description: Biological neurons in our brain communicate by sending electrical spikes (pulses), but our computers do not. Can we learn something from our brain functioning to speed up our computers? This PhD project will address this fundamental question by analysing spike signals from both biological neurons and from artificial neurons made of memristors. A memristor is the fourth basic element for electrical circuits, which was missed for a very long time and was experimentally discovered by Dr. Stanley Williams – an outstanding scientist who is also a member of the £1M EPSRC project [short non-technical description of the project is here] led by a Loughborough team of academics from different disciplines (Physics, Chemistry, Computer Science). This project also provides a unique opportunity for the PhD student to work with a world-leading, cross-continental team of researchers from the Salk Institute for biological studies (USA), University of Massachusetts Amherst (USA), and ARM Ltd.

The PhD student will simulate electrical circuits of different complexity using stochastic differential equations and analyse large neuromorphic circuits using the Fokker-Plank equation. Different spiking regimes (chaotic, stochastic, periodic) will be studied and the best systems for realisation of a “spiking computer” will be optimised for fabrication and measurements by both the project experimental team and our industry partners.     

Figure taken from Z. Wang, S. Joshi, S. Savel’ev et al., Fully memristive neural networks for pattern classification with unsupervised learning, Nature Electronics 1, 137–145 (2018).

How to apply:

Applications should be made online at Under programme name, select Physics.

Quote reference number: SS/PH/2020

Closing date is 14th February 2020.

Theory and design of active quantum circuit elements

Supervisor: Alexandre Zagoskin (; phone+44-15-09-223306).

Nature of work: The work will consist of theory and numerical simulations.

Area: Quantum engineering.

Potential implications: Quantum sensing/imaging/communications. Quantum simulations.

Brief description: Until the pioneering work of Chua in 1971, it was taken for granted that there are three lumped circuit elements (two reactive: inductor, capacitor, and one active:  resistor), which allow to describe an arbitrary electric circuit. From the considerations of symmetry, Chua showed that there must exist the fourth such element, memristor, which can be considered as a resistor with memory. A realisation of such a structure was only achieved in the early 2000s.

Development of quantum technologies since the 2000s produced qubit-based quantum analogues of capacitors and inductors, which can be in a quantum superposition of states with different values of C and L, respectively. It seems that there exist no quantum analogues to resistors and memristors, because of the inherent dissipation. Nevertheless, such analogues are possible due to nonlocality of transport in mesoscopic structures. You will work on developing their quantitative theory and modelling experimental protocols for their investigation.   

How to apply:

All applications should be made online at  Under programme name, select Physics.

Please quote reference number: AZ/PH/2020.  Closing date is 14th February 2020.