Supervisor: Dr Tunde Csoban

This project will investigate the challenges and solutions related to missing data in real life datasets, which often occur due to factors such as patient availability, equipment and staff constraints, or participants not feeling comfortable with disclosing sensitive information. You will explore some traditional and/or machine learning imputation techniques, apply these methods to a dataset, and assess how they impact correlation between variables, and simple regression models.  

Supervisor: Professor Adam Kleczkowski

Coffee is an important produce in Thailand, yet it is under threat from climate change, pests and diseases. With the increase in temperature, the production might not be sustainable or will require structural changes. In this project, we will use data analytics, including regression and Machine Learning, to carry out a spatial analysis of climate suitability for coffee production in Thailand. The student will analyse the weather data, both the past records and future projections, and predict possible shifts in production areas. Coffee is also threatened by pests and diseases, most prominently, a coffee borer. The project will also incorporate climate suitability maps for the insect occurrence and estimate the risk of infestation.

Please note that this is an Advanced Data Science project.

Supervisor: Professor Adam Kleczkowski

Ash dieback, Hymenoscyphus fraxineus, is a fungal disease that has been inflicting devastating impacts on the UK landscapes and biodiversity since its first detection in 2012. As there is no promising treatment or prevention measure, the best hope for the long-term future of the UK's ash trees lies in identifying tolerant or resistant trees for breeding new generations. This project combines unique field data sets with modelling approaches. In this project, the student will first analyse data for a selection of provenances and blocks, identifying the disease progress in each individual tree. The next step will involve constructing and analysing a Markov chain model, to identify the natural variability between trees.

This is an Advanced Mathematical/Computational Modelling and Data Science project.

Supervisor: Professor Adam Kleczkowski

Large parts of agriculture in Asia and Africa are threatened by existing as well as emerging Pests and Diseases, a trend further exacerbated by climate change. Chemical control is widely used for P&Ds, but the rise of microbial resistance and health and environmental concerns mean that finding resistant cultivars becomes a priority. This project will use a combination of data analysis and modelling to assist in non-invasive detection of viruses in plants at early stages of infection. The modelling will be applied to Sterility Mosaic Disease (SMD), an economically important pathogen of legumes (pigeonpea), in collaboration with partners in India. We will use a combination of statistical analysis and modelling (including a hierarchical Bayesian approach) to capture within- and between-cultivar variability in the response to SMD.

This is an Advanced Data Science and Mathematical/Computational Modelling project.

Supervisor: Professor Chris Robertson

This analysis will investigate the relationship between vaccine uptake rate and rurality and deprivation with any spatial and elements that may occur. This will be conducted by using Bayesian conditional autoregressive (CAR) models for both spatial and temporal modelling. This modelling will be done using R Studio, making use of spaal packages such as CARBayes and CARBayesST. 

Project aims

• to learn about and understand statistical models for spatial variation. This is complex work for a 3rd year student
• to analyse spatial trends in Influenza vaccine uptake among (a) those aged 65+ and (b) those aged 18-64 who are in clinical risk groups and are also eligible for influenza vaccination in Scotland
• to fit spatial models to vaccine uptake and estimate and test for the presence of spatial correlation
• to make use of social and census data at the Scottish Governments open data platform to find and derive appropriate variables which might explain the spatial trends
• to fit spatial regression models to explain the pattern of spatial variation in influenza vaccine uptake

Supervisor: Professor Chris Robertson

Following the outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus and the subsequent global spread of the 2019 novel coronavirus disease (COVID-19), health systems and the populations who use them have faced unprecedented challenges. This study will re-analyse the impact of COVID-19 on the uptake of hospital-based care in Scotland.  An interrupted time-series analysis focusing on change points will be used to evaluate the impact of these events on hospital services at a national level and across demographics, clinical specialties and National Health Service Health Boards.

Supervisor: Professor Chris Robertson

A number of study designs are used to estimated vaccine effects in the population. These include, cohort studies, case control studies, test negative designs and target trial analysis. This project will use simulated population data to compare the estimates using different study designs.

Supervisor: Professor Chris Robertson

This project will use numerical and simulation methods to model the pattern of influenza circulation in Scotland and it control through vaccination. The analysis will be based upon a simple SEIR transmission dynamic model using parameter estimates derived from a literature review.

Supervisor: Dr Yue Wu

Randomising numerical methods for solving differential equations introduces randomness into traditional solvers to enhance accuracy, stability, and computational efficiency. This approach involves incorporating random perturbations into numerical schemes, such as Euler or Runge-Kutta methods, to reduce bias, control error accumulation, or explore multiple solution trajectories. By leveraging probabilistic techniques, these methods can provide improved approximations in cases where standard solvers struggle, such as stiff systems, chaotic dynamics, or high-dimensional differential equations. Randomised numerical methods have found applications in various fields, including uncertainty quantification, stochastic modeling, and scientific computing, where they help address the limitations of classical approaches.

This project will explore different randomisation strategies, analyse their theoretical properties, and implement them in practical scenarios, offering you a unique opportunity to engage with cutting-edge numerical techniques that blend probability theory and differential equations.

Supervisor: Dr Yue Wu

This project explores signature methods from rough path theory and their applications in time series analysis.

Rough path theory provides a mathematically rigorous framework for understanding complex, irregularly sampled, and highly variable sequential data by extending classical calculus to paths with low regularity. The signature of a path provides a structured and efficient way to represent sequential data by capturing its underlying dynamics through iterated integrals. This approach is particularly useful in handling complex, high-dimensional, or irregularly sampled time series, where traditional methods often fall short. By leveraging signature features, we can enhance machine learning models for tasks such as financial forecasting, medical diagnosis, and speech analysis.

You will gain hands-on experience with rough path theory, computational techniques, and real-world applications, making this an exciting opportunity to develop expertise in an advanced mathematical tool with broad practical impact.

Supervisor: Dr Michael Grinfeld

Insurance companies collect insurance payments from their clients and so can be sure of their income, but they cannot control the payout of premiums. This situation creates very interesting stochastic process in which an important question is the computation of the time to ruin of an insurance company. This problem is very intimately related to a novel class of random walks (with replentishment on the boundary). In the project, you will learn about the theory of risk under random premiums, will simulate such systems and will draw conclusions about random walks using the results obtained for insurance processes.

For this project we look for you to have knowledge of some probability theory as well as programming experience.

Supervisor: Dr Michael Grinfeld

The classical Moran process is one the most fundamental processes in population genetics. As such, it has to conserve population numbers and so is obliged to couple reproduction and death events, which is not biologically realistic. Recently, we have developed a lax cell number control version of the process and the project will explore numerically its properties such as fixation probability and mean fixation time. 

We look for you to have knowledge of some probability theory as well as programming experience.

Supervisor: Dr Michael Grinfeld

Phase separation is a solid-solid phase transition process in metallic alloys which can have disastrous consequences (as in aeroplane wings). There are many sophisticated models of phase separation, such as the famous Cahn-Hilliard one. The project will explore numerically a much simpler discrete model of phase separation, which still retains the flavour of the more complicated partial differential equation ones.

For this project we look for you to have programming experience.

Supervisor: Dr Michael Grinfeld

Many processes such as spread of innovations, epidemics or populations, are described by travelling waves of reaction-advection-diffusion equations. Being able to compute the speed of propagation of such a travelling wave is an important applied problem. Very often there is an interval of allowable speeds and the object of interest is the minimal allowable speed. The project will involve learning about methods for finding and approximating (also numerically) such minimal speeds.

Some differential equations exposure as well as programming experience is required for this project

Supervisor: Dr Mohammud Foondun

The binomial model is a fundamental tool in financial mathematics for pricing options, which are financial derivatives. This project introduces you to the basics of option pricing by constructing a step-by-step binomial tree to model the possible future prices of an underlying asset. You will learn how to calculate the fair price of European and American options using risk-neutral probabilities and backward induction. The project will also explore the relationship between the binomial model and the famous Black-Scholes formula. By the end, you will gain hands-on experience in implementing the binomial model using programming tools like Python or R.

Supervisor: Dr Mohammud Foondun

Queuing theory is the mathematical study of waiting lines or queues, with applications in fields like telecommunications, traffic engineering, and customer service. This project will introduce you to basic queuing models, such as the M/M/1 queue, where arrivals and service times follow exponential distributions. You will analyze key performance metrics like average waiting time, queue length, and system utilization. The project will also involve simulating queuing systems using software tools to visualize and interpret results. By the end, you will understand how queuing theory can optimize real-world systems and improve efficiency.

Supervisor: Dr Mohammud Foondun

Deep learning is a subset of machine learning that uses neural networks with multiple layers to model complex patterns in data. This project will provide you with a foundational understanding of deep learning concepts, such as feedforward networks, backpropagation, and activation functions. you will implement a simple deep learning model using frameworks like TensorFlow or PyTorch to solve a basic classification or regression problem. By the end, you will have a practical understanding of how deep learning algorithms work and their potential impact on technology.

Supervisor: Dr Mohammud Foondun

Probability puzzles and paradoxes are fascinating problems that often challenge our intuition and understanding of chance. This project will explore classic puzzles like the Monty Hall problem, the Birthday Paradox, and Bertrand's Paradox, using both theoretical and simulation-based approaches. You will learn how to analyze these problems using principles of probability theory and computational tools like Python or R. The project will also highlight the importance of careful reasoning and assumptions in solving probability problems. By the end, you will gain a deeper appreciation for the beauty and complexity of probability through engaging and thought-provoking examples.

Supervisor: Connor Watret

Mental health disorders are becoming increasingly discussed and explored on the impact they have on individuals. This project aims to investigate the spatial distribution of mental health disorders recorded in the United States (US) to investigate potential relationships between states and mental health disorders. We will explore how to analyse spatially dependent data utilising shapefiles to create visual map representations in R, perform appropriate spatial tests and investigate potential models to predict future trends. The project will provide insights into state-level patterns on mental health and the effectiveness of spatial analysis in public health research.

Supervisor: Dr Elizabeth Dombi

Cryptographic systems are divided into two main families: symmetric-key and asymmetric key (public-key) systems. Symmetric-key cryptography uses the same key for encryption and decryption and one of its main challenges is for the parties to exchange the secret key. Public-key cryptography uses two keys, an encryption key which is made public and a decryption key which is kept secret. RSA is the most widely used public-key cryptographic system that was developed in 1977 by Rivest, Shamir and Adleman. 

This project could investigate the number theoretical background needed to understand public-key algorithms, describe how RSA works and consider possible attacks against RSA

Supervisor: Dr Elizabeth Dombi

Game theory provides a mathematical framework to model and analyse situations in which two or more parties make competitive or collaborative strategic decisions. These situations are abundant in everyday life and therefore game theory became one of the most important areas of applied mathematics having applications in economics, politics, social sciences, psychology or biology to name but a few.

Games can be classified in several ways. For example, characteristics can include the number of players, whether participants have perfect or imperfect information, whether players have conflicting interests or if there is scope for negotiations. John von Neumann and Oskar von Morgenstern, the founding fathers of game theory, first considered the mathematical description and analysis of two-person zero-sum games in The Theory of Games and Ecomonic Behaviour. 

This  project could explore key concepts in game theory and the mathematical formulation of two-person zero-sum games,  von Neumann's Minimax theorem and explore the computation of optimal strategies

Supervisor: Dr Jiazhu Pan

Statistical techniques and concepts of time series analysis are applied to empirical analysis of financial markets. The software R is used as a vehicle for presenting practical implementations from financial modelling. The application includes calculation of value-at risk and expected shortfall.

Supervisor: Professor David Greenhalgh

The project will look at the formulation of epidemic models and the concept of the basic reproduction number, R0. It will look at the calculation of the basic reproduction number both by direct calculation and by the next generation matrix method. The project will look at equilibrium analyses of these models and local, and if appropriate, global stability analysis of the disease-free equilibrium. Then the model will look at local stability analysis of the endemic equilibrium. You will write a MATLAB program to integrate the differential equations using realistic parameter values.

Supervisor: Dr Ainsley Miller

This project aims to explore the relationship between socioeconomic factors—such as income, education level, and social class—and work-related stress in employed Scottish adults. We will also examine how these factors influence mental health outcomes, particularly anxiety and depression. By analysing variables such as household income, occupational status, educational qualifications and socioeconomic status (SIMD quintiles), this research seeks to understand how these factors contribute to varying levels of work stress.

Additionally, this project assesses work conditions—such as work-life balance, unrealistic time pressures, and workplace relationships—for their impact on work-related stress outcomes. Lifestyle factors such as alcohol consumption, self-reported general health and Vitamin D supplement use are also considered for their role in controlling stress and mental health.

Supervisor: Dr Ainsley Miller

PCOS is considered to be the most common endocrine disorder in women of reproductive-age. Endocrine disorders occur when the endocrine system, which is responsible for hormone production, does not function correctly. The cause of PCOS is not known (although it is thought to be genetic) and there is no cure for PCOS.

The overall aim of this project is to model instances of polycystic ovary syndrome (PCOS). This project can be carried out using traditional statistical means or machine learning techniques (or a combination of both).

Supervisor: Dr Ainsley Miller

A stroke is a serious life-threatening medical condition that happens when the blood supply to part of the brain is cut off. An ischemic stroke occurs when a vessel supplying blood to the brain is obstructed. It accounts for about 87% of all strokes. Whereas a haemorrhagic stroke occurs when a weakened vessel ruptures and bleeds into the surrounding brain. The blood accumulates and compresses the surrounding brain tissue.

The overall aim of this project is to model the instances of strokes (both ischemic and haemorrhagic) using two different modelling techniques. These techniques will then be compared based on various metrics to determine which is better.

Supervisor: Dr Philip Knight

One cannot ignore the networks we are part of, that surround us in everyday life. There's our network of family and friends; the transport network; the telephone network; the distribution network shops use to bring us things to buy; and so on. Analysis of networks particularly the huge networks that drive the global economy (directly or indirectly) is a vital science and we propose in this project that the you will review the available tools for analysis and apply them to a particular class of networks chosen from:

• Social networks
• The internet
• Biological networks
• Epidemics on networks

Supervisor: Dr Philip Knight

Matrix balancing involves multiplying a matrix on the left and right by diagonal matrices to achieve prescribed row and column sums. There is a very simple way of achieving this which has been rediscovered many times but the algorithms properties are still being investigated and improvements are regularly suggested. This project will look at algorithms for achieving balance and their applications. Application areas include machine learning, network analysis, fair voting, bioinformatics and actuarial science.

Supervisor: Ken Chen

Deep learning is quickly entering the syllabi of mathematical degrees, because it is modern, fast and yet closely related to nonlinear optimization and numerical solution techniques. This summer project will start from learning how Python work in solving simple maths problems and then move onto simple data science problems, in the framework of supervised learning. The key concepts of convolution and activation will be considered.  The project will end by predicting solutions to image problems that require no training data. Example codes and data will be given to assist understanding. There is also an opportunity to work with UK undergraduate students in a small team. 

Supervisor: Dr Ryan Stewart

The Scottish Health Survey is a large-scale government survey which collects data annually form the Scottish population living in private households, both on young people and adults. Many variables have been recorded on different aspects of health and lifestyle. Many organisations may be interested in detailed analyses from the results of the survey. The student(s) will find out about the survey and choose which data to analyse. The student would then report their key findings. This can be adapted to suit different populations.

Supervisor: Dr Ryan Stewart

The Alzheimer's Society found that in 2019, there were over 850,000 people with dementia in the UK. This represents 1 in every 14 of the population aged 65 years and over. In 2040, there will be over 1.5 million people with dementia in the UK, at the current rate of prevalence. This project will use statistical techniques to model dementia (Alzheimer's and Vascular) in the UK. this could be adapted to other populations and health conditions.

Supervisor: Dr Ryan Stewart

The Public Health Scotland COVID-19 dashboard proved highly successful and useful for displaying a range of custom plots and tables. This idea could be replicated with other data sets. This project aims to create a R Shiny dashboard based on other publicly available data. The dashboard would be user friendly and accessible and allow users to create a range of interactive tables and plots at their requisition. Experience and proficiency in R is required. 

Supervisor: Dr Ryan Stewart

The use of data in professional sports has increased substantially over the past couple of decades, and this includes tennis which has been increasingly following this trend, with data being used extensively by players, coaches, tournament organisers, broadcasters, betting companies and spectators. This project involves analysing match data from professional tennis matches in the ATP World Tour over one or more years, using publicly available statistics from across all tournament matches to determine how some of the key match statistics relate to match results. Analysis of these match statistics may be used to determine the factors most strongly associated with winning matches for use in match prediction, identify characteristics of certain matches such as "upsets" (where the favourite or highly ranked player loses to someone significantly lower rated), or compare aspects of the tennis tour across different periods. This could be adapted to other sports.

Supervisor: Dr Sebastian Sprick

The use of hydrogen as an energy carrier has the potential to radically reduce greenhouse gas emissions from hydrocarbon combustion as its use in fuel cells does not emit any CO2 at the point of use. Photocatalytic water splitting has the potential to produce hydrogen from water using sunlight with oxygen as the only side product and in recent years conjugated polymers have been extensively explored as photocatalysts. Significant progress has been made understanding structure–property relationships in conjugated polymer photocatalysts, however, there are only few reports of overall water splitting. Instead much of the focus has been on driving hydrogen evolution as a half-reaction using as so-called hole-scavengers (typically amines). These sacrificial systems are very efficient, but have an overall negative energy balance, that means more energy is used in making the amine compared to what is released in the form of hydrogen gas. To overcome this the project aims to explore different types of renewable biomass as potential hole-scavengers. A range of methods for pretreatments will be explored and a small set of photocatalysts will be made and used to test the hydrogen production rates of the pretreated biomass samples exploring future potential.

Supervisor: Dr David Nelson

The selection of ligands for nickel catalysis is still largely based on trial and error or expensive, time-consuming reaction screening. We are approaching the problem differently, by building a picture of how the ligand attached to nickel affects each key step of the putative catalytic cycle, so that ligands can be selected more rationally. 

The project can be either experimental (laboratory-based) or computational (using our ARCHIE-WeSt high-performance computing facility). Experimental projects might involve the synthesis of model nickel(0) or nickel(II) complexes and analysis of their reactivity, including using kinetic studies, or the monitoring or study of prototypical catalytic reactions. Computational projects would either involve the study of a fundamental step or a specific ligand system, and might seek to explain experimental results, make predictions, or test different computational chemistry workflows (such as nudged elastic band transition state searches, ONIOM methods for larger and more complex systems, or composite methods for rapid yet accurate calculations).
While this work is fundamental organometallic chemistry and catalysis, the results will have an impact on the development of reactions for synthetic organic chemistry.

Supervisor: Professor Clare Hoskins

Anticancer agents are often completely insoluble in aqueous media. In order for these drugs to be administered either by injection or oral administration, they must first be solubilised in a delivery vehicle. Lipid nanoparticles have undergone a huge resurgence in interest after the success of the COVID vaccination programmes worldwide. Solid lipid nanoparticles can be fabricated from inexpensive lipids and easily tailored for application based on their constituent components. Drugs may be incorporated into the nanoparticle matrix which allows for enhanced solubilisation or trafficking properties. The major advantage of these technologies over traditional liposome technologies is their enhanced stability and ability to form solid dosage forms. These can be surface modified for specific tissue targeting or prolonged circulation depending on the need.

This project seeks to fabricate an optimum solid lipid nanoparticle platform for solubilisation of anticancer drug compounds. Formulations produced will be characterised using standard analytical techniques including particle size, microscopy, drug loading and release profiles under various simulated physiological conditions. Should time permit, the formulations will also be tested for their biological performance in vitro in colorectal cancer cell lines.

There are 2 places available and the project duration is 10 weeks.

Supervisor: Professor Craig Jamieson

In the UK, one in eight men will get diagnosed with prostate cancer and despite decades of research it remains the second most common cancer. With the advent of modern screening techniques for early diagnosis along with pioneering treatments the survival rate has increased over the last four decades. Treatments pursued are in the form of androgen deprivation therapy achieved through surgical and/or chemical castration. Although this initially leads to tumour regression, a significant proportion of patient’s relapse, and this leads to castrate-resistant prostate cancer. 

The androgen receptor (AR) is responsible for growth and maintenance of both normal and cancerous prostate tissue. Deletion studies involving the N-terminus domain (NTD) have proven it to be essential for receptor function, making it as appealing as it is challenging as a drug target. However, the main challenge that impedes drug development is how intrinsically disordered the domain is, which means it is not amenable for structure-based drug design.

Our laboratories are interested in the design and synthesis of small molecules that would target the NTD of the AR. The AR team have developed a range of scaffolds that require further exploration, however, back-up approaches are required. This project will focus on the synthesis and evaluation a fragment-based approach to targeting the AR-NTD.  In particular, we seek to apply the emerging concept of reactive fragments,  in order to expediently identify new templates which covalently modify the protein domain of interest which may then be elaborated into a validated hit series.

This internship is ideally suited if you have a strong background and knowledge in synthetic organic chemistry and willing to work as part of a team towards the synthesis of new fragment libraries for the screening against this important molecular target.

Supervisor: Dr Kristin Ceniccola-Campos

A rapidly evolving illicit drug supply has necessitated novel approaches to assess the prevalence of new psychoactive substances (NPS) in the community. While existing testing protocols provide critical public health information, little is known about the distribution and accumulation of NPS and their metabolites in open water sources and aquatic environments. Global implementation of wastewater-based epidemiology approaches to monitor consumption patterns has proven useful but require further study for many NPS, in particular street formulations of benzodiazepines. 

In this project, you will develop a method to identify and quantify benzodiazepines in surface water sampled from river tributaries. This will include the isolation of target substances by solid-phase extraction followed by analysis using liquid chromatography – time-of-flight mass spectrometry. The project will involve a combination of sample collection, experimental work, and subsequent data analysis.

Supervisor: Dr Aaron Lau

The Lau group is working on the design, synthesis, and functional characterization of peptide and peptide-mimetic (“peptoid”) sequences for a range of applications, including as antimicrobial therapeutics and as self-assembled nanomaterials. Specific sequences of natural and non-natural amino acids encode for these different functionalities. An iterative solid-phase synthesis (SPS) protocol is used to couple amino acids in the correct order and sequence length, and the resulting molecules are purified by HPLC and characterized by mass spectrometry coupled with HPLC/UPLC analysis. Depending on interest, a student may choose to focus on the solid phase synthesis, analytical chemistry (analysis and purification) or physical characterization aspects of the project (for example, nano-assembly DLS size, solubility, and surface charge measurements), and gain hands-on experience in the interdisciplinary themes of synthetic chemistry, nanomaterials, or drug discovery of the Lau group. A project may span different activities appropriate to the research theme and the time available (such as synthesis and nano-assembly characterization). In all cases, you may choose either an 8 or 10 week project and you will shadow a PhD or post-doctoral lab mentor currently engaged in the research theme.

Supervisor: Dr Marc Reid

In this multidisciplinary project, you will use innovative camera technology to design new colorimetric tests for illicit substances and emerging drugs of abuse.

Available digital camera technologies present boundless opportunities to pioneer chemical colour analysis tools based on computer vision. Such imaging techniques are attractive to chemists, chemical engineers, and biologists due to the simplicity, scalability, and low cost compared to more established analytical methods. Quantification of real-time colour changes also creates opportunities to bring Chemistry and Computer Science together in unexplored ways.

Your project is an opportunity to combine synthetic and forensic chemistry with state-of-the-art colour analysis tools under development in our team. Your work will involve the synthesis of potential chemo-sensors, and the analysis of the colour changes as recorded in photos and videos. These chemo-sensors will be tested using illicit substances, with a view to develop identification spot tests which might be used in a forensic setting. You will also have the opportunity to explore your spot test design strategy using a new liquid handling robot recently installed in our laboratory.

Supervisor: Dr David Palmer

The earlier detection of cancer is vital to improve patient survival, since earlier diagnosis and treatment can maximize the opportunity to combat or control disease progression.   Liquid biopsies are the holy grail of early cancer detection, providing diagnosis from a simple blood test. There are multiple tests under development that can identify a wide range of molecular features that may be indicative of cancer.   

One promising method for earlier cancer detection is the use of infrared spectroscopy. When biological samples are irradiated with infrared light, it causes the molecules within it to vibrate. These vibrations occur at distinct frequencies which can be visualised as an infrared spectrum. Peaks within this spectrum are a ‘fingerprint’ of the biomolecules contained within the sample. There are subtle differences in the spectra of cancer and non-cancer patients which can be elucidated with artificial intelligence.

You will develop new machine learning methods for the early detection of cancers from FTIR spectra of blood serum samples.  The project will provide training in analytical chemistry, artificial intelligence, cancer diagnostics, chemometrics/data science, and modelling of vibrational spectroscopy data.  The studentship will be based in the Strathclyde Computational and Theoretical Chemistry Hub (SCoTCH), a centre for excellence in computational molecular science.  The centre occupies modern computational laboratories with access to high-performance computing facilities including graphic processing units (GPUs). The project will benefit from alignment with the company, Dxcover Ltd.

Supervisor: Dr Gavin Craig

Metal-organic polyhedra (MOPs) are discrete molecules that can show permanent porosity. Packing in the solid state of MOPs is often dominated by weak dispersion forces, meaning that it can be difficult to control the extrinsic porosity associated with the cages. In addition, the cages often have a tendency to hydrolyse and fall apart, making them unsuitable for processing into materials.

Hypotheses & objectives

The tendency of MOPs to fall apart is due to the nature of the coordinate bond between the ligand and metal node. With this project, we aim to strengthen these bonds, to yield robust cages. The objectives of the project are to:

• synthesise novel ligands and MOPs
• characterise them using a variety of physical techniques
• use gas sorption measurements to prove gas uptake

Experimental approach

Initial work will focus on ligand synthesis, tuning the structure to give specific backbones that will lead to porous cages. We will then proceed to probe reactivity with a range of transition metals, aiming to crystallise the materials and obtain their structure through single crystal X-ray diffraction. Once bulk purity is established, we can then perform measurement of the cage porosity.

Prospective tasks

The majority of the internship will be used to synthesise new ligands and metal-organic polyhedra, and perform their characterisation. In the first instance, this will include infrared and nuclear magnetic resonance spectroscopy, thermogravimetric analysis, and single crystal and powder x-ray diffraction. There may also be opportunities for gas sorption measurement, depending on the project progress. You will also participate in general group activities such as group meetings.

Supervisor: Professor Tell Tuttle

You will leverage coarse-grain molecular dynamics simulations in combination with generative AI approaches to explore the co-assembly of peptide-based materials. By employing coarse-grained models, the research will efficiently capture large-scale self-assembly behaviour while maintaining key molecular interactions. Generative AI techniques, such as machine learning-driven molecular design and optimization, will be used to predict novel peptide sequences with tailored self-assembly properties. This integrated approach will enable the rapid screening of peptide formulations, offering insights into the structural and functional properties of co-assembled peptide systems, with potential applications in biomaterials, nanotechnology, and sustainable materials development.

Supervisor: Dr Tahereh Nematiaram

Molecules have unique chemical and physical behaviours, such as how they interact with other molecules, their electronic properties, or their stability. Traditionally, these behaviours are predicted using complex quantum-chemical calculations, but can AI make similar predictions faster?

In this project, you will train a machine learning model to predict specific molecular behaviours based on available data. Using a dataset of molecular properties, they will test whether AI can classify molecules into different categories (such as conductive vs non-conductive, stable vs unstable). By analysing the model’s accuracy and learning which molecular features are most important, you will explore how AI can assist chemists in material discovery.

This project is inspired by a recently published study from our group that demonstrated how machine learning can be used to predict charge transport properties in molecular crystals without costly quantum-chemical calculations. By applying similar AI techniques, students will investigate whether AI can extend beyond charge transport and predict broader molecular behaviours, helping to accelerate materials discovery and molecular design.

The duration of the project is 10 weeks and we can accommodate 2 students.

Supervisor: Dr Rebecca Beveridge

Whilst mass spectrometry is most widely known for its ability to measure small molecules, it can also provide highly valuable information on intact proteins, including their range of conformations. It is especially suited to the measurement of dynamic proteins, also called intrinsically disordered proteins (IDPs), that exist in many different conformations. Moreover, when combined with a related technique called ion mobility (ion mobility-mass spectrometry, IM-MS) the overall size of the different conformations can be measured, in terms of their rotationally averaged collision cross sections (CCS) given in in Å2.

This project involves the use of IMMS to study the behaviour of IDPs. These proteins are important to study because they’re highly abundant in the human proteome, with around one third of protein sequences predicted to be disordered. Moreover, IDPs are highly involved in diseases such as cancer and neurodegenerative diseases. However despite their importance, their dynamic nature renders them challenging to characterize with most biophysical methods. 

You will gain experience in sample preparation for mass spectrometry, acquiring mass spectrometry and ion mobility data, and data analysis techniques. You will learn about protein conformations and dynamics, and the ways in which sequence mutations can change protein behaviour and result in disease states. This project will be suitable for candidates with an interest in mass spectrometry/ analytical chemistry or protein biophysics/ chemical biology. 

Supervisor: Dr Francesco Papoff

Quantum dot nanolasers are distinguished by their compact size, low thermal load, and operation governed by quantum effects. Phenomena such as quantum correlations between photons and emitters, as well as superradiance, play a critical role in the onset of lasing factors that previous models have failed to capture. In this project, you will explore and apply advanced methods to derive the second-order correlation function of the nanolaser. This function is a key characteristic of any light source, offering crucial insights into the underlying emission processes driving light generation.

Supervisor: Professor Thorsten Ackeman

External cavity diode lasers (ECL) are a versatile and relatively inexpensive lasers source for addressing narrow atomic lines in the near infrared spectrum and are hence the established workhorse for quantum technologies based on alkaline atoms like Rb or Cs. These ECL are built from off-the-shelf laser diodes with optical feedback from a grating. They are stabilized against frequency drifts via saturable absorption spectroscopy. The project will set up two ECL for cooling and trapping of Rb atoms, set up the frequency stabilization and characterize their stability. Depending on progress, extensions in the direction of setting up acousto-optical modulators for frequency shifting and fast switch on/off of the light are possible.

Aim

Set up a tunable laser and frequency stabilization to enable spectroscopy and nonlinear optical measurements in Rb vapour.

Tasks

• setting up and aligning optical setup
• small electronic soldering tasks
• understanding PDI controllers
• data analysis