Materials and Processes

A major challenge for defence & security laser systems is managing the heat generated and differential thermal expansion of the different components in the system. A compounding challenge is the vast range of ambient temperatures in which these systems need to operate without thermal distortion. However, additive manufacturing gives the unique opportunity to tailor the material properties, and design the support structures, to manage different thermal expansions of optics, laser crystals, laser diodes, etc.

The research will focus on developing new material combinations in powder-bed laser fusion additive manufacturing, as well as to design and demonstrate new mechanical 3D printed structures, with the specific aim of managing and matching the thermal expansion coefficients of laser system components, while maintaining the required precision and stability of these high-end laser systems.

This project will be jointly supervised by:

• Prof Jonathan Corney j.r.corney@ed.ac.uk
• Dr Sam Tammas-Williams s.tammaswilliams@ed.ac.uk

Smart Products Made Smarter

The PhD project forms part of a larger Prosperity Partnership Programme, Smart Products Made Smarter, a collaboration with Heriot-Watt University, University of Edinburgh and Leonardo.

We are pleased to invite applications for a PhD studentship to work as part of a leading team of experts. This studentship will be supported by an enhanced stipend of £20,716 per year over 3.5 years.

This grant, sponsored by the EPSRC, is a collaboration between academia and Leonardo. There are currently PhD opportunities available to work on diverse topics as part of this collaborative team. The work will involve strong links with industry.

The research addresses a broad range of challenges. These challenges exemplify future product lifecycle management from smart concept, design, development and manufacture to enhanced end-user capability, united by a common digital thread to enable smarter products to be made smarter. Each challenge area has clearly identified initial research themes and associated research challenges to be addressed and these are indicated below:

Challenge 1 (C1) the Making challenge: To create new hybrid manufacturing processes, that combine multiple Additive Manufacturing (AM) process with precision machining and coating processes to create components that disrupt the traditional functional trade-offs of Size, Weight and Power (SWaP) through techniques such as varying the material properties within a part and harnessing the digital production of optical components.

Challenge 2 (C2) the Manipulation challenge: To create new handling processes that fully exploit the digital data flows which define custom components whose shape and functionality is tailored to production by dexterous, highly adaptable robots that are programmed dynamically.

Challenge 3 (C3) the Computation challenge: To create new signal processing & machine learning methodologies that enable intelligent, digital & connected sensor products while mitigating the data deluge from the multiple sensors produced by Leonardo operating across the EM spectrum.

The themes represent areas that could form the basis of your PhD. These PhD positions offer great flexibility and we welcome the opportunity to explore other ideas & themes.

Please note that this advert will close as soon as a suitable candidate is found. 

The University of Edinburgh is committed to equality of opportunity for all its staff and students, and promotes a culture of inclusivity: https://www.ed.ac.uk/equality-diversity

Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Please note that as this is a defence based project, only UK/EU students are eligible to apply. International applicants are not eligible.

Further information on English language requirements for EU/Overseas applicants.

Tuition fees + stipend are available for applicants who qualify as:

   a UK applicant   an EU applicant (International/non EU students are not eligible)

Funding is available through EPSRC Prosperity Partnership Programme. As this is a defence related project there are nationality restrictions (see above).

Further information and other funding options.

Recent research has shown that aqueous amino acid salts (AAS) are effective carbon capture solutions. They offer advantages over the usual amine-based carbon capture solutions due to their reduced toxicity, corrosivity, volatility and cost whilst also having good stability and high capacity.

Especially, it has been shown that some AAS solutions phase separate after absorbing CO2. Importantly, phase separating carbon capture solutions could lead to reduced regeneration energy penalties for carbon capture processes since the regenerate can easily be separated and has reduced water content. Thus, AAS solutions are promising candidates for both direct air capture (DAC) and post-combustion (PC) carbon capture processes.

Moreover, it is also known that some aqueous AA solutions exhibit microphase separation. Although the mechanism is currently unknown, this phenomenon is thought to be important in crystallization processes. It might also have implications for the origin of life since AAs and carbon dioxide are thought to be key ingredients of the primordial soup.

This project aims to understand the key driving forces that lead to both bulk and microphase separating AA and AAS solutions, especially for carbon capture processes. The successful student will use molecular simulations to model and understand this behaviour at the microscale.

It is expected that the applicant will have a good degree in Engineering, Physics, Chemistry, Mathematics, or any other related subject. We are particularly keen to hear from applicants who want to develop expertise in the molecular simulation of fluids. Prior experience in this area is useful but not a requirement.

The successful student, depending on eligibility, will have opportunities for teaching and further training with in the university, as well as participation in the intellectual community provided by the School of Engineering’s Institute for Materials and Processes, in which they will be based.

The University of Edinburgh is committed to equality of opportunity for all its staff and students, and promotes a culture of inclusivity. Please see details here: https://www.ed.ac.uk/equality-diversity

Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in Engineering, Physics, Chemistry, Mathematics, or any other related subject possibly supported by an MSc Degree. Prior experience in molecular simulation of fluids is useful but not a requirement.

Further information on English language requirements for EU/Overseas applicants.

Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere.

Further information and other funding options.

A wide spectrum of target thermoplastic materials can be manufactured by tailoring polymer blends to achieve particular combinations of end-user performance (e.g. mechanical, electrical, structural support) properties, important in various industrial sectors, especially in extreme-condition environments. These polymeric material classes include the widely used polyolefins (PE, PP), but also others derived from higher-MW monomers (PS, PVC, PVP, PC, PTFE, etc. ). The key challenge here is to computationally predict (and experimentally confirm) optimal blend compositions which can be manufactured reasonably easily (high processability via extrusion, blow moulding, etc.), in order to achieve formulated products which achieve (or exceed) the said end-user properties, under reasonable total (fixed+operating) cost per unit mass.

Over the years, the UoE Polymer Engineering Laboratory has compiled a wealth of experimental datasets for many blend-condition combinations (virgin/recycled feedstocks, input molecules, temperatures, extrusion/moulding settings, product macro-dimensions) e.g. Polymers 2023, 15(21), 4200 (https://doi.org/10.3390/polym15214200). Constructing first-principles mathematical models which combine macromolecular physical chemistry (e.g. Flory-Huggins theory) descriptions with mass/heat balances towards end-product property estimation, rigorous unit operation (e.g. extruder) design and optimisation is extremely cumbersome, both due to mathematical complexity, but even more so due to the extreme and pervasive parametric uncertainty hampering such efforts.

Therefore, this PhD project aims to combine state-of-the-art Artificial Intelligence (AI) and Machine Learning (ML) methodologies in order to explore optimal blending and processing conditions for polymeric material classes, towards developing materials which will achieve high performance indices for key target properties, while also ensuring high processability and cost-optimal manufacturing at scale.

Strong computational skills, an interest in statistics, and prior experience in numerical methods/software (MATLAB, Python) are essential; prior ML-based project work is desirable.

https://www.eng.ed.ac.uk/about/people/dr-dimitrios-i-gerogiorgis

https://vasileioskoutsos.wixsite.com/softmaterials

The University of Edinburgh is committed to equality of opportunity for all its staff and students, and promotes a culture of inclusivity. Please see details here: https://www.ed.ac.uk/equality-diversity

An undergraduate degree in Chemical/Mechanical Engineering, or a closely related area (Physics, Chemistry), with a strong background in computational modelling.

Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. 

Further information on English language requirements for EU/Overseas applicants.

Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere.

Further information and other funding options.

Fully-funded PhD studentship available on nanosensor engineering by electrocrystallisation with the opportunity to start immediately. The overall goal of the project is to create a platform technology for nanosensor scale up by understanding the nanoscale phenomena in electrodeposition. Electroanalytical and nanomaterials characterisation methods will be used to investigate the nucleation and crystal growth mechanisms of charge-transfer complexes (CTCs) on ultramicroelectrodes and nanoelectrode patterns. The knowledge gained will be used to achieve controlled electrodeposition of CTC nanowire sensors on microchips. This project will contribute to UK’s global competitiveness in high-tech areas such as advanced manufacturing of wearable microelectronics and the internet-of-things sensors.

Electrodeposition is used by electroplating industry to deposit monolayers, thin films, and thick coatings. Understanding of electrochemical nucleation and crystal growth at the nanoscale is necessary for widening the adoption of electrodeposition by high-tech industries such as energy storage, advanced electrode materials, and sensing. Precise electrodeposition of nanowires and thin films on microchips holds the potential for scalable manufacturing of nanosensors.

This project seeks to address a significant knowledge gap related to electrodeposition at the nanoscale, with a focus on CTCs from the tetrathiafulvalene (TTF) and tetracyanoquinodimethane (TCNQ) family. Recent progress in nanomaterials characterisation and simulations reveals an intricate process involving nanocluster building blocks, their interactions, and multistep crystallisation pathways. Electrodeposition provides a unique means to study early-stage crystallisation because of the additional control provided by the applied overpotential. To bridge this knowledge gap, the project outlines specific aims: (1) obtain dynamic structural data on early-stage CTC electrocrystallisation through real-time microscopic and electrochemical measurements integrating the ultramicroelectrode technique; (2) scale up findings from single ultramicroelectrodes to nanoelectrode arrays; and (3) demonstrate impact on technology by creating gas nanosensors using CTC electrodeposition.

If successful, you will have the opportunity to work under the supervision of Professor Guangzhao Mao, an internationally recognised scientist and the Head of School of Engineering at The University of Edinburgh.

The University of Edinburgh is committed to equality of opportunity for all its staff and students, and promotes a culture of inclusivity. Please see details here: https://www.ed.ac.uk/equality-diversity

Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Further information on English language requirements for EU/Overseas applicants.

Tuition fees + stipend are available for Home/EU and International students.

Applications are also welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere.

Further information and other funding options.

Food texture is related to the way our senses perceive and feel the rheological and mechanical properties of edible substances. For example a potato chip is crispy; an apple is crunchy; butter is soft; bread is firm; candy is hard; yogurt is smooth; cream is thick; cake is moist, and honey is sticky. Food texture is critical for the consumer and impacts on a product’s market share. It is affected by the composition, manufacturing process, storage conditions and aging. It impacts the final quality and nutrition value of the food product. Food industry strives to improve texture while enhancing the product’s nutritional value and health benefits. For example, healthy oleogels can be used in substitution of harmful trans/saturated fats while retaining the sense of a “mouthful” product.

Texture is complex to quantify, as it is the result of interplay of the food mechanical and rheological properties as physically sensed in the mouth. It is the result of the complex movement of chewing involving our jaws, teeth, and tongue, and the combined comminution (particle size distribution change) and gradual dissolution of substances in saliva.

This project aims to develop and combine mechanical and rheological testing methodologies that will characterize texture rapidly and reliably, in real time, during the manufacturing and storage period. The experimental program will be complemented by state-of-the-art artificial intelligence (AI) and machine learning (ML) methodologies in order to correlate improved texture with optimized manufacturing and storage processes.

The ideal candidate will combine strong experimental and computational skills, an interest in food science and engineering, mechanics, rheology and numerical methods/software (e.g., MATLAB, Python).

https://vasileioskoutsos.wixsite.com/softmaterials

www.eng.ed.ac.uk/about/people/dr-dimitrios-i-gerogiorgis

The University of Edinburgh is committed to equality of opportunity for all its staff and students, and promotes a culture of inclusivity. Please see details here: https://www.ed.ac.uk/equality-diversity

An undergraduate degree in Chemical/Mechanical Engineering, or a closely related area (Physics, Chemistry), with a strong background in computational modelling.

Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree.

Further information on English language requirements for EU/Overseas applicants.

Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere

Further information and other funding options.

The conversion of biomass into value-added platform chemicals can greatly alleviate the reliance on fossil feedstock. Biomass alcohol such as benzyl alcohol is one of the important biomass-derived platform compounds and a significant feedstock, which are used to produce a series of value-added derivatives, such as benzaldehyde, hydrobenzoin, benzoin, and deoxybenzoin. Among these derivatives, the C-C coupling chemicals, hydrobenzoin, benzoin, and deoxybenzoin have higher value than the rest and can be used as the versatile intermediates for the production of high value-added downstream products, such as various chemical additives, dyestuff, pharmaceuticals as well as the precursor of photo initiator. However, in the conventional process, the synthesis of above-mentioned C-C coupling products focuses on sharpless asymmetric dihydroxylation of alkenes, and requires the use of toxic cyanide and metal complex catalysts, resulting in a host of by-products, therefore limits the development of the traditional biomass alcohol conversion.

Photocatalysis has recently emerged as a promising way for benzyl alcohol conversion because of the low energy consumption, mild reaction conditions and high selectivity. However, the reaction mainly promoted by UV light, which is a very small portion of sunlight resource. This project will develop visible-light driven photocatalysts with suitable band levels to facilitate sunlight absorbtion and create separated electrons and holes for redox reactions, particularly the photocatalytic coupling of benzyl alcohol to C-C coupling compounds with non-toxic and high selectivity catalytic process. In addition, it is important to improve the student the experimental skill, materials characterization skill and data analysis skill.

Student who joins our group will learn the fundamentals of photocatalytic reactions and photocatalyst synthesis, the use of GC, BET, FTIR, GCMS, XPS, SEM and chemical analysis to understand the reaction mechanisms. Furthermore, the student will be trained in the critical analysis of experimental data, material characterization and writing skills.

Primary objectives:

1. Train the student with photocatalytic experimental skills.

2. Teach the student how to synthesise photocatalysts.

3. Develop the student with analytical skills of experimental data and characterization, the use of GC, BET, FTIR, GCMS, XPS, SEM and chemical analysis to understand the reaction mechanisms.

4. Improve the student with critical thinking and writing abilities.

5. Reduce the by-product benzaldehyde and increase the yields of C-C coupling products.

6. Increase the selectivity of a specific product (hydrobenzoin, deoxybenzoin or benzoin).

7. Understand the mechanism of the photocatalytic C-C coupling reaction.

The University of Edinburgh is committed to equality of opportunity for all its staff and students, and promotes a culture of inclusivity. Please see details here: https://www.ed.ac.uk/equality-diversity

Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Further information on English language requirements for EU/Overseas applicants.

Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere.

Further information and other funding options.

Approximately 2 million individuals globally suffer from kidney failure, necessitating treatment options such as transplantation and dialysis. Transplantation is limited by donor availability, forcing many to rely on HD. Whereas transplant recipients exhibit approximately 80% survival rates five years post-procedure, those undergoing HD have less than a 50% chance of surviving the same period due to what's known as “residual uremic syndrome” resulting from the incomplete removal of certain uremic toxins during HD, significantly contributing to the higher mortality observed in these patients.

Current HD technologies rely on membranes which are limited by size, thus unable to effectively eliminate larger uremic toxins from the patient's bloodstream. This approach lacks precision and effectiveness as it is designed on small molecules like urea and fails to address other, more harmful toxins.

The first crucial step is to clearly identify the metabolites associated with adverse effects. This task can be addressed using a combination of metabolomics and AI. Metabolomics can detect a wide range of metabolites, some of which may play critical roles in the health outcomes of patients with kidney failure.

Three studies have investigated the link between serum metabolites and mortality in patients with kidney disease, but they have yielded inconsistent results regarding which metabolites are implicated, underscoring the need for further research. The integration of metabolomics with AI may also enhance our understanding of the mechanisms: this deeper insight is essential for developing more effective HD treatments that can mitigate the adverse effects. A comprehensive AI-based analysis of the existing data is essential, laying the groundwork for future large-scale metabolomics research. However, identifying these metabolites is just the initial step. The ultimate goal is to leverage this information to enhance dialysis treatments by developing materials capable of efficiently capturing the most toxic molecules. AI has the potential to expedite the exploration of the vast materials space. Achieving both the identification of harmful metabolites and the development of effective materials is an ambitious task, given the multitude of toxins and materials involved.

Fortunately, AI technologies can greatly accelerate progress towards these dual objectives.

https://www.bbc.com/news/uk-scotland-edinburgh-east-fife-67156562

Such study will allow to correlate specific materials features (e.g. chemical composition and porosity descriptors) to the ability of the filtering materials of removing toxic molecules correlated to mortality. This work will provide guidelines for material synthesis and/or selection in the design of more efficient and tailored HD treatment which can reduce patients’ mortality.

References: [1] The Kidney Project, University of California San Francisco, https://pharm.ucsf.edu/kidney [2] S. Al Awadhi et al, A Metabolomics Approach to Identify Metabolites Associated With Mortality in Patients Receiving Maintenance Hemodialysis, Kidney Int Rep 2024 9, 2718–26. [3] S. Kalim et al., A Plasma Long‐Chain Acylcarnitine Predicts Cardiovascular Mortality in Incident Dialysis Patients, J American Heart Association 2, 2013. [4] Hu, J.-R., et al Serum Metabolites and Cardiac Death in Patients on Hemodialysis, Clin J Am Society of Nephrology 14(5): 747-749, 2019. [5] https://nurturebiobank.org/, visited on 4th October 2024 [6] T. Fabiani et al., In silico screening of nanoporous materials for urea removal in hemodialysis applications, Phys. Chem. Chem. Phys., 2023, 25, 24069. [7] REDIAL, redefining hemodialysis with data-driven materials innovation, project https://www.suspromgroup.eng.ed.ac.uk/redial

https://www.suspromgroup.eng.ed.ac.uk/redial

https://www.ai4biomed.io/research/projects-2025/#cmsm

https://www.ai4biomed.io/

Application deadline: 20 January 2025

We offer fully funded 4-year studentships, covering tuition fees, stipend (£19,237 in 2024/25) and an individual budget for travel and research costs. There are also allowances for sick pay, maternity leave and other purposes. Funding has open eligibility regardless of your nationality and domicile.

The CDT offers additional funding for public engagement activities, evaluation experiments and research visits.

Each student will also benefit from state-of-the-art facilities, including unique data and computational resources. The CDT has access to EPCC facilities including the University’s HPC Centre of excellence offering unique AI capability (Cerebras CS-1/CS2, Graphcore Bow Pod), supercomputing (ARCHER2, DiRAC, Cirrus), and analytics platforms, assisted by over 100 technology experts. EIDF will provide research compute capacity for AI via a new cluster of 136 Nvidia A-100 GPU cards.

The University of Edinburgh is committed to equality of opportunity for all its staff and students, and promotes a culture of inclusivity. Please see details here: https://www.ed.ac.uk/equality-diversity

Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Further information on English language requirements for EU/Overseas applicants.

Tuition fees + stipend are available for Home/EU and International students

CDT AI in Biomedical Innovation Conditions

We offer fully funded 4-year studentships, covering tuition fees, stipend (£19,237 in 2024/25) and an individual budget for travel and research costs. There are also allowances for sick pay, maternity leave and other purposes. Funding has open eligibility regardless of your nationality and domicile.

The CDT offers additional funding for public engagement activities, evaluation experiments and research visits.

Each student will also benefit from state-of-the-art facilities, including unique data and computational resources. The CDT has access to EPCC facilities including the University’s HPC Centre of excellence offering unique AI capability (Cerebras CS-1/CS2, Graphcore Bow Pod), supercomputing (ARCHER2, DiRAC, Cirrus), and analytics platforms, assisted by over 100 technology experts. EIDF will provide research compute capacity for AI via a new cluster of 136 Nvidia A-100 GPU cards.

Further information and other funding options.

https://suspromgroup.eng.ed.ac.uk/

I joined the University of Edinburgh in 2020 as Chair in Thermodynamics of Materials and Processes after working 15 years at the University of Bologna, Italy‚ where I hold an Associate Professorship in Chemical Engineering. My international experience includes research stays at the North Carolina State University (USA), National Technical University of Athens (Greece), Universidad Nacional del Sur (Argentina), University of Melbourne (Australia). My work is focused on the study and development of materials, processes and simulation methods for fluid separations, CO₂ capture, biofuels upgrading, water purification, packaging, biomedical processes.   The research approach is problem-oriented and adopts a systematic strategy that encompasses experimental testing, molecular, macroscopic and multiscale modeling tools.  

Go to the  Group SusProM Website 

-PhD in Chemical Engineering, 2002, University of Bologna -Master Degree in Chemical Engineering, 1998, University of Bologna

-Chair of the Working Party on Thermodynamics and Transport Properties, European Federation of Chemical Engineers (EFCE) , 2022-present 

-Treasurer and Vice President, European Membrane Society Council, 2019-2023  

Associate Member of IChemE Member of AIDIC (Italian Association of Chemical Engineering) Member of European Membrane Society Member of AIChE

-Member of the Editorial Board of Membranes

-Editor of the Special Issue "Fundamentals of Transport in Polymers and Membranes—Honorary Issue for Professor Giulio C. Sarti" 2022 

-Editor of the Special Issue "Gas Transport in Glassy Polymers" 2020-2021

-Watch my webinar “Membranes for CO₂ Capture: Thermodynamic aspects” given during the EFCE Spotlight Talks, December 3rd 2020.  Organized by the European Federation of Chemical Engineers. -Host of the European Membrane Society Live Webinars Series, watch them on Youtube