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Postgraduate Teaching Assistant, School of Science

Job grade: Specialist and Supporting Academic (SSA) Grade 5 

Fixed Term for 5 years, or until completion of a PhD, whichever is sooner.

Combining a teaching role with postgraduate research, this PGTA position is an exciting opportunity to complete a PhD under the supervision of Loughborough University's expert staff while gaining invaluable experience in the delivery of learning and teaching.

This PGTA position sits within the School of Science at Loughborough University and will support our provision of statistics support throughout the university.  Teaching roles will include 

  • Running existing statistics workshops for the Doctoral College and developing new workshops based on an identification of needs.  
  • Providing drop-in statistical support and advice for students on modules and projects through the Mathematics Learning Support Centre (MLSC).
  • Providing one-on-one statistical support sessions through the Statistical Advisory Service (SAS).

Candidates should have a good background in experimental design and statistics, including data handling, exploratory and graphical data summaries, a range of techniques for statistical modelling and inference and best practice in writing up statistical methodology and results.  There will be opportunities within the role to undertake additional training in specialist statistical topics to complement existing expertise in the SAS.

Studentship tuition fees will be paid by the University but as the post will attract a full-time SSA Grade 5 salary (24,285 - £28,936) no additional studentship stipend will be paid. Progression opportunities exist within the post as there is the potential to progress to SSA Grade 6 on gaining Associate Fellow status of the Higher Education Academy (or other such body) plus meeting additional criteria after two years of employment.

Only individuals with existing rights to work and study in the UK can be considered as the role does not meet the requirements for visa sponsorship under UK immigration regulations.

Please note that there are two application forms to complete for the post:

Informal enquiries should be made to Dr Eugenie Hunsicker, Director of Service Teaching by email at or by telephone on 01509 222875.

Closing date Sunday 19 November, with preliminary skype and full panel interviews planned for week commencing 4 December.

Due to studentship requirements, start date must be 1 January 2018

Further information:

Job description

Operating Procedures for Post-Graduate Teaching Assistant

The PhD topic undertaken by the PGTA must fall within one of the Centres in the School of Science:  The Mathematics Education Centre, the Centre for Imaging Science  and the Interdisciplinary Centre for Mathematical Modelling.

A list of potential topics and supervisors in these Centres is given below.  

Candidates should contact a member of staff in one of the Centres to discuss these or other PhD topic options.

Statistics of quasicrystal (QC) formation (Supervisor:A. Archer)

This project will be associated with the Interdisciplinary Centre for Mathematical Modelling.

This is a project relating statistics and mathematical modelling of quasicrystals. It has two parts: (i) to develop a Lattice-Boltzmann computer simulation code to simulate the dynamics of colloidal particles/nanoparticles suspended in a liquid. We will use this to study particles with interactions tuned to form QC structures and investigate the influence of the hydrodynamic interactions on the QC formation. (ii) Perform a statistical analysis of both the spatial and temporal correlations between the particles in such systems. We expect these to be particularly interesting in cases where the QC forms for dynamic reasons, but is actually metastable with respect to the regular crystal and is not the minimum (free) energy state.

Supervisor: Professor Andrew Archer

Nonlinear Dynamics of Quantum Coherent Structures (Supervisors:A. Zagoskin, A. Balanov)

This project will be associated with the Interdisciplinary Centre for Mathematical Modelling

The development of new quantum technologies since the beginning of the century enabled the experimental realization of quantum coherent structures containing from dozens to thousands quantum bits. Such structures are expected to find multiple key technological applications, but their development is hindered by fundamental difficulties in theoretical description and experimental evaluation of their performance.

In this project you will conduct a theoretical investigation of the phenomena emerging from the nonlinear dynamics of such structures treated as open quantum systems, using a combination of analytical and numerical techniques. While current research effort is mostly focussed on relaxation to the equilibrium states, the proposed project will encompass a number of nontrivial dynamical phenomena such as chaos, self-oscillations, noise-induced order etc. The main aim is to investigate the transition from an arbitrary initial state to a stationary regime in the presence of decoherence and external noise and the evolution of entanglement and quantum coherence during this process. The statistical tools for discriminating different regimes based on a limited set of data, in particular with respect to the entanglement characteristics of the system, will be developed.

The results are expected to be applied to quantum computers, annealers, metamaterials, sensors and other devices containing quantum coherent arrays.

Entry requirements:
Applicants should have, or expect to achieve, at least a 2:1 Honours degree (or equivalent) in Physics or Mathematical Sciences.
A relevant Master’s degree and / or experience in one or more of the following will be an advantage: Theoretical Physics, Quantum Information, Applied Mathematics, Dynamical Systems.

[1] A. Zagoskin, "Superconducting Quantum Metamaterials," in Nonlinear, Tunable and Active Metamaterials; Shadrivov, I.V.; Lapine, M.; Kivshar, Yu.S. (eds.),Springer, 2015, p. 255.

[2] C. Neill et al., Nature Physics 12, 1037–1041 (2016) doi:10.1038/nphys3830.     [3] A. Zagoskin, “Quantum Engineering”, Cambridge University Press, 2011.

Supervisor: Dr Alexandre Zagoskin, Dr Alexander Balanov

Modelling, design and testing of arrays-based superconducting sensors for magnetic imaging devices (Supervisor: B. Chesca)

This project will be associated with the Centre for Imaging Science.

Within this PDTA studentship we are going to model, design, fabricate and test superconducting magnetic sensors based on coherent operation of hundreds of Josephson junctions arrays. Such sensors will be used to improve performances of magnetic imaging devices currently used in medicine (MEG-magnetoencephalography-MEG and magnetocardiography-MCG, low-field MRI), geology (oil oil prospecting, mineral exploration), non-destructive evaluation (airplane, construction and food industries), physics (magnetometers) and astronomy (X-ray detectors). The project consists of modelling, design, fabrication and characterization of various advanced architectures of parallel-series arrays of Josephson junctions to build the next generation of highly-sensitive superconducting magnetic sensors. Modelling, design and characterization will be done at Loughborough University while fabrication will be done at the state of the art multi-million nano-fabrication facilities at Nottingham University. The PDTA student will develop a comprehensive simulation package to model electric transport properties of series/parallel arrays of Josephson junctions. The task is to find the optimized arrays parameters: values for inductances, resistances, mutual inductances, pick-up coils, superconducting loops, etc. Then the PDTA student will implement the optimized parameters provided by the numerical simulations into practical designs using CAD software. The PDTA student will then proceed with fabrication (micro-photolithography at Nottingham University) and testing (electric transport properties at low temperatures 2K-100K and small magnetic fields (0-100) microT at the centre for ultra-sensitive electrical characterization laboratory at Loughborough University.


This PDTA studentship will consolidate the Loughborough position as the UK-leading group in the field of superconducting sensors for magnetic imaging.


Since the discovery of superconductivity in 1911, superconducting electronics has been identified as the future for electronic devices. Superconducting devices have several advantages over semiconducting ones like, high switching speed, low power dissipation and low noise. However, despite these advantages, superconducting electronics became established only in niche applications. The main reasons for this fact are:

a)    the absence of a superconducting transistor with sufficient current and voltage gain, high speed, good isolation between input and output, and reasonable manufacturability;

b)     impracticability/high cost required by operation at very low temperature: 10mK-4.2 K

Recent developments reported in my group may change this situation dramatically as explained below.

Loughborough: UK-leading group in superconducting sensors for magnetic imaging

With 8 publications [1-8]  in the last 4 years that include 3 in Applied Physics Letters, 3 in Superconducting Science and Technology, 1 in Nature-research highlights and several press releases [9-11] my group plays a leading role the UK in the area of modelling/fabrication of superconducting sensors for magnetic imaging. Several scientific highlights are summarized below.

Superconducting Quantum Interference Devices (SQUIDs) as the most sensitive magnetic sensors and are being routinely used in many areas, among them: medicine, research in material science (for measurements of the magnetic properties of materials), geology, astronomy. At present due to their superior flux noise performances in the vast majority of applications, SQUIDs made of low temperature superconductor (LTS) SQUIDs operating at 4.2K are being used. This is despite several significant advantages high temperature superconductor (HTS) SQUIDs operating at 77K offer: low cost and user friendly cooling procedures and potential superiority as magnetic imaging devices due a reduced separation between the sensors and the room temperature object under study (because of the decreased thermal insulation demand). As reported in [3-5, 9-11] we have developed a new generation of superconducting magnetometers based on non-interacting large SQUID arrays operating flux-coherently at 77K with superior noise resolution than the best single-SQUID-based magnetometers operating at 4.2 K. Such magnetometers are ideal candidates to replace single-SQUIDs in many applications.

We build parallel arrays [6] that have the ability to amplify the self-induced electromagnetic radiation and are therefore natural GHz/THz generators/sensors the ideal candidates to build GHz/THz imagining devices.

We dramatically improved (by more than 5 times) the gain of superconducting transistors operating at temperatures of 77K and above [8]. This opens the door to fabricate hybrid superconducting-semiconducting electronics for improved performances.


  1. B. Chesca, D. John, R. Pollett, M. Gaifullin, J. Cox, C. Mellor, S. Savelev, Appl. Phys. Lett. 111, 062602 (2017).
    1. B. Chesca, invited paper,  Supercond. Sci. Technol. 29, 080501 (2016).
    2. D. Castelvecchi and B. Chesca, Nature, Research Highlights 526, 613 (2015).
    3. B. Chesca, invited paper for Superconductivity News Forum Global Edition, HP101, (2015). 
    4. B. Chesca, D. John and C.J. Mellor, Appl. Phys. Lett. 107, 162602 (2015).
    5. B. Chesca, D. John and C.J. Mellor, Supercond. Sci. Technol. 27, 085015 (2014).
    6. B. Chesca, D. John and C.J. Mellor, Supercond. Sci. Technol. 27, 055019 (2014).
    7. B. Chesca, D. John, M. Kemp, J. Brown, and C.J. Mellor, Appl. Phys. Lett. 103, 092601 (2013).

 Press Releases

  1. American Institute of Physics, AIP, 20th of October 2015.

10.Spectrum IEEE, 22th of October 2015

11.MedicalPhysicsWeb, 14th December 2015

Supervisor: Dr Boris Chesca

Gravitational Waves and Wormholes (Supervisor: Prof. F. Kusmartsev)

This project will be associated with the Centre of Interdisciplinary Mathematical Modeling.

The  project concerns the interaction of gravitational waves with other objects, in particular, with elementary particles and with various astrophysical bodies. These interactions are described in the framework of the Einstein general relativity. The candidate will solve the Einstein equations with the aim of finding  new observable effects associated with the interaction of these objects with gravitational waves. The equations will be studied as well using variational methods in the framework of the Lagrangian approach.  Various universe spaces with different geometry such  as AdS and universes with wormholes will be considered.  We anticipate finding new effects related to nontrivial interactions of gravitational waves with gravitating bodies, especially when dark energy and dark matter are taken into account. The obtained results will be compared with LIGO experiments. The issue of the black holes inspiral will be also addressed.

We expect to publish the results in journals with high impact factors such as Journal of the High Energy Physics ( IF=6.2). More details given in the reference below.

Wu, W., Pierpoint, M., Forrester, D.  and Feo Kusmartsev,. J. High Energ. Phys. (2016) 2016: 17. doi:10.1007/JHEP10(2016)017

Supervisor: Professor Feo Kusmartsev

Developing 'number sense' in young children (Supervisors: M. Inglis and D. Hewitt)

This project will be associated with the Mathematics Education Centre.

In recent years numerical cognition researchers have made a great deal of progress in understanding different routes by which children can assign meaning to number symbols. This has led several research groups to propose and test different interventions to improve children's "number sense". Typically these approaches focus on helping children to associate magnitudes with numerical symbols, for example by pairing nonsymbolic arrays of objects or dots with Arabic numerals (a so-called cardinal approach: "6" paired with a picture of six dogs). But in recent years some work in numerical cognition has suggested that an ordinal approach is also worth exploring. Here meaning is assigned by creating relationships between symbols (for example "6" gets its meaning from it's relationship to 5 and 7). The goal of this project is to design and test a classroom intervention based on ordinal symbol-symbol associations using a device known as the Gattegno Tens Chart. The student will need to develop a familiarity with the numerical cognition literature, but also have an interest in the design and evaluation of classroom interventions. 

Supervisors: Dr Matthew Inglis, Dr Dave Hewitt

High resolution imaging and analysis of metals in eye disease (Supervisor: A. Managh)

This project will be associated with the Centre for Imaging Science.

Age related macular degeneration is the most common cause of sight loss in the developed world. The imbalance of trace metals, such as zinc, copper and iron, in the eye has long been linked with progression of the condition, with some studies suggesting that zinc supplementation may be beneficial. To fully assess the impact of supplementation requires detailed analysis of the distribution of metals within the tissue - a challenging task considering the small scale features present within the retina! Loughborough University, in collaboration with industry, have developed a world leading system for imaging metals, which is based on laser ablation – inductively coupled plasma – mass spectrometry. With its high speed, high sensitivity and sub-micron spatial resolution, the prototype system is ideally placed to image small retinal features. Development of this analytical technology is producing ever larger and more complex data sets, with in excess of half a million data points now generated for every minute of analysis. The project will therefore aim to develop statistical protocols that will recognise, extract and interpret the relevant information from the high resolution images generated.

Supervisor: Dr Amy Managh

Effective teaching and learning of statistics (Supervisors: I. Xenidou-Dervou, N. Attridge)

This project is associated with the Mathematics Education Centre.

Almost all disciplines involve the use of statistics, e.g. Medicine, Psychology, Engineering, Business, Chemistry, Sports Science etc. It is also important for everyday life and reasoning, as well as for enterprise especially with the increasing importance of data analytic skills and analytical reasoning in the work place. This project will, therefore, focus on effective teaching and learning of statistics.

Storage and retrieval of information are equally important for learning. Effective storage relies on connecting new information to existing knowledge, meaning that learning must be active; we must interpret, elaborate and connect information to store it most effectively. “Gappy notes” or “guided notes” (Konrad, Joseph, & Eveleigh, 2009) is a teaching method which is assumed to improve storage via increasing engagement during lectures, structuring note-taking, directing attention and ensuring the students have a complete set of notes to study from.

Once information is stored, it is essential that we can retrieve it. An “important peculiarity” (Bjork & Bjork, 1992) of memory is that retrieving information from memory once makes it easier to retrieve it again in the future (Bjork, Dunlosky & Kornell, 2013), particularly when the retrieval was difficult (e.g., after a delay). Retrieval practice (i.e. practice in retrieving information from memory) can be implemented in several ways, including answering questions in class (Nunes & Weinstein, 2012) or while studying (Weinstein, McDermott & Roediger, 2010).

This project will combine the fields of guided notes and retrieval practise with the aim of developing effective teaching and learning methods for statistics. For example, it will investigate the effectiveness of quiz questions in class for learning statistics, considering factors such as delay between learning and testing, procedural versus conceptual questions, and open-ended versus multiple choice questions.

The student will read the relevant mathematics education and cognitive psychology literature, design experiments, collect data from participants in lectures and lab studies, conduct and interpret statistical analyses on the data, and write findings for publication.

Bjork RA, Bjork EL. (1992). A new theory of disuse and an old theory of stimulus fluctuation. In From Learning Processes to Cognitive Processes: Essays in Honor of William K. Estes, ed. A Healy, S Kosslyn, R Shiffrin, vol. 2, pp. 35–67. Hillsdale, NJ: Erlbaum

Bjork, R. A., Dunlosky, J., & Kornell, N. (2013). Self-regulated learning: Beliefs, techniques, and illusions. Annual Review of Psychology, 64, 417-444.

Konrad, M., Joseph, L. M., & Eveleigh, E. (2009). A meta-analytic review of guided notes. Education and Treatment of Children, 32, 421–444.

Nunes, L. D., & Weinstein, Y. (2012). Testing improves true recall and protects against the build-up of proactive interference without increasing false recall. Memory, 20, 138-154.

Weinstein, Y., McDermott, K. B., & Roediger III, H. L. (2010). A comparison of study strategies for passages: rereading, answering questions, and generating questions. Journal of Experimental Psychology: Applied, 16, 308-316.

Weinstein, Y., Nunes, L. D., & Karpicke, J. D. (2016). On the placement of practice questions during study. Journal of Experimental Psychology: Applied, 22(1), 72-84.

Supervisors: Dr Iro Xenidou-Dervou, Dr Nina Attridge

Non-destructive Bayesian learning of material density function, with applications to the inversion of scanning electron microscopy image data of nuclear graphite (Supervisor: D. Chakrabarty)

The non-destructive learning of the density function in the bulk of a material sample is a harder-thanusual inverse problem that however promises multiple real-life applications. In a recent publication Chakrabarty et. al (2015), presented a Bayesian methodology to learn the unknown material density of a given sample by inverting its image data, where each pixel in the image results from a sequence of contractive projections of the convolution of the unknown material density function and the unknown microscopy correction function, that they also learn from the data. The learning of these unknown functions was therefore undertaken using multiple and sequential inversions of the image data, where such data resulted from a novel design of imaging experiment that involved imaging at multiple values of a parameter that controls the sub-surface depth from which information about the density function is carried. Chakrabarty et. al (2015) recall that real-life material density functions are typically characterised by high density contrasts and are highly discontinuous, implying that they exhibit correlation structures that do not vary smoothly. Then, in the absence of training data–as is typically the case–modelling such correlation structures of real material density functions is not possible, and this led the authors to the learning of large, discrete samples from the unknown functions (using Markov-Chain-Monte-Carlo-based inference techniques).

In the proposed Ph.D project, inversion of images taken with Scanning Electron Microscopes (SEMs) will be undertaken using the methodology of Chakrabarty et. al (2015), to learn such discretized versions of the unknown sub-surface material density and microscopy correction functions, which will then be treated as the training datasets, generated at the chosen locations inside the material sample (design points), at which the aforementioned learning was undertaken. Then modelling each unknown function as a realisation from a Gaussian Process, we will Bayesianly learn the function. The images used for the development and application of this methodology will consist of SEM images of nuclear graphite, that is being studied under a current EPSRC-funded project, with HW as the PI, and RS as a CI. We will employ such image data to determine and characterise changes in nuclear graphite, following its usage in a reactor where it is subjected to intense radiation.  

This is a real-life application that is of very high potential importance to multiple stake-holders, including the National Nuclear Laboratory. Nuclear graphite contains randomly distributed pores and cracks, and the material undergoes creep and swelling after irradiation. The morphology of the pores is subject to alteration under irradiation, and can affect performance. These changes will be identified up to subsurface
depths of several tens of micrometres, from the learning of the material density function using the Bayesian methodology delineated above, allowing for greater understanding–and thereby greater control–of the performance of nuclear reactors, thus paving the way for the next generation of reactors.

Related to teaching:
DC who is proposed to be the main supervisor, is a Bayesian statistician, and is well placed to contribute to the design of the PGTA’s teaching duties and capacity, including guiding the PGTA to the teaching of existing Statistics modules (both as part of service teaching, and teaching within Mathematics), as well as consider the introduction of the very useful modules of Bayesian Statistics and Bayesian Inference,
which are currently missing from university-wide curricula. These modules could potentially be considered part of a taught-session for doctoral students enrolling in one of the Centres in the University, such as the Centre for Imaging Sciences.

Related to research themes:
The project involves topical concepts within Mathematical and Computational Statistics, using large, high-dimensional image data. Thus, it fits very well into the remit of Centres within the School of Science, in addition to the promise of linking to other Schools within the University. Electron microscopy is used by many researchers associated with the main university research themes.

Related to enterprise:
There is a strong enterprise potential in the project owing to its relevance to users such as the National Nuclear Laboratory, and commercialisation of any software that is developed as part of the project, is another a viable option.

Chakrabarty, Dalia et. al, “Bayesian Density Estimation via Multiple Sequential Inversions of 2-D
Images with Application in Electron Microscopy”, 2015, Technometrics, 57, 2, pg.217–233.

Supervisor: Dr Dalia Chakrabarty

Polymer Dynamics in Translocation and Spatially Confined Systems (Supervisor: T. Ala-Nissila)

This project will be associated with the Interdisciplinary Centre for Mathematical Modelling.

Dynamical behavior of biopolymers under spatial constraints is an important theoretical problem with applications in many fields of future technologies, such as rapid sequencing of the human DNA and nanofluidics. An outstanding problem in this field is the translocation dynamics of biopolymers though nanopores driven by external fields. It is a far-from-equilibrium process described by the tension propagation (TP) theory [1], which is analytically formulated through the Brownian Dynamics TP (BDTP) formulation [2]. In this project the aim is to generalize the BDTP theory to various externally driven translocation protocols and carry out corresponding molecular dynamics simulations to benchmark and test the theoretical predictions. The theory will also be extended to include hydrodynamic interactions and relevant electrostatic correlations.

1. T. Sakaue, Phys. Rev. E 76, 021803 (2007). 2. J. Sarabadani, T. Ikonen and T. Ala-Nissila, J. Chem. Phys. 141, 214907 (2014).

Supervisor: Professor Tapio Ala-Nissila