Guest post by Aakansha Dhawan, MSc Machine Learning in Science 2023

Coming from a computer science background, I always had a strong foundation in algorithms and programming. However, I was eager to delve deeper into the evolving world of artificial intelligence, which led me to pursue the MSc in Machine Learning in Science at the University of Nottingham. The program’s blend of theoretical coursework and hands-on projects made it an ideal choice to sharpen my skills in machine learning techniques and their scientific applications.

While studying, I also worked part-time as a Data Analyst and ML Researcher at Oak Brook. This role provided me with valuable real-world experience where I could apply machine learning algorithms to solve practical business problems, such as customer segmentation, churn prediction, and process automation. I tackled diverse challenges, including working with noisy datasets and optimizing model performance, which refined my skills in data preprocessing, feature selection, and hyperparameter tuning.

One of the highlights of the program was an interdisciplinary project where I applied natural language processing (NLP) techniques to analyze financial data, extracting trends and predicting sentiment around certain events. This project gave me a chance to explore different machine learning approaches, such as recurrent neural networks (RNNs) and transformers, and understand their strengths in handling sequence data.

The supportive learning environment at Nottingham was invaluable, featuring expert faculty members who guided me through cutting-edge research in areas like deep learning and reinforcement learning. The opportunity to collaborate with peers from various scientific disciplines also broadened my understanding of how machine learning could be applied across diverse fields, including healthcare, finance, and environmental science.

Completing the MSc program laid a solid foundation for my career in AI. The skills and experiences I gained helped me secure a research internship focused on deep learning applications in computer vision, and later, a full-time position at a leading technology company. I now work on developing AI solutions for real-time data processing and predictive analytics, applying the knowledge and insights I gained during my time at Nottingham every day.

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Guest post by Elliot Hicks, MSc Machine Learning in Science 2022

When I was studying for my BSc in Physics at the University of Manchester, I decided that I wanted a masters that would give me the best possible advantage for entering the field of machine learning. When I came across the Machine Learning in Science masters, it was an obvious fit for me, and I applied the same week.

The MLiS course at Nottingham was the best time I’ve had in academia, the content, staff and projects were fantastic. One of the main things I liked about the course was the consistent freedom in projects – if I wanted to learn about reinforcement learning, I simply chose a coursework that lent itself to a reinforcement learning solution. This freedom not only allowed me to learn the best approaches for applying machine learning to real-world problems, but also allowed me to find what niches of machine learning I enjoyed most.

During my time on the MLiS course, I was fortunate enough to be invited to assist some of the MLiS staff on a project with an external company, Oakbrook Finance. There I went on to research, train and evaluate a cutting-edge NLP model for sentiment analysis. This work provided invaluable opportunities for me as I was able to connect with and present findings to Oakbrook staff in an industry environment. Crucially, this was my first piece of work experience in the field of machine learning and it gave me a huge advantage when applying for positions after I graduated.

Having had the pleasure of working alongside some of the MLiS staff, and being taught by the rest of the team, I cannot recommend them enough. The MLiS staff’s expertise covers a wide range of topics, and they work hard to be available to support students whenever possible. Even now, a year on, I still like to stay in contact with staff from the course (and sometimes I still ask for advice when I’m stuck!)

Following the masters, I secured an internship working as a deep learning researcher where I trained physics informed neural networks for industrial applications. I even got to present to large companies like Google and NVIDIA. Now, I work as a machine learning engineer at a healthcare AI company where I work on computer vision algorithms for medicine. The advantages the MLiS course gave me when joining the industry can’t be overstated, I use the content I learned on this course every day at work. I have no doubt that the wide range of knowledge, skills, and experiences that this course gave me were indispensable to the start of my career.

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Guest post by Dr. Karen Mullinger, who recently set up a webpage showcasing careers in medical physics.   

In my opinion Medical Physics is fascinating because of the real world difference it can make and the Medical Physics related careers that are possible: from the NHS, to industry, to academia. During the pandemic I realised through the endless video conference calling that there was an opportunity to create some engaging material to showcase Medical Physics. The idea was to benefit people at all stages of their study/career paths from starting to think about coming to university to trying to decide what to do after their undergraduate/post-graduate degrees.

As we know NHS staff are extremely stretched and unlikely to be able to travel to Nottingham to give talks (plus this limits who can benefit from the talk). However, many people are happy to take 10-15 mins out of their day to benefit others. Therefore light-bulb moment: I could record interviews with NHS staff at a time of day that suited them and they could be where-ever they needed to be providing they could access a computer and the internet! I could then give people an insight into the role of Medical Physics in the NHS.

I chose to target two groups with very different but highly linked roles in the NHS for these videos. I contacted clinical doctors benefiting from the equipment we learn about during the first year course (e.g. ultrasound, MRI, PET, EEG). I interviewed them as to the benefits the underlying medical physics brings to patients. I also contacted University of Nottingham Alumni who have gone on to work in Medical Physics related disciplines as NHS Clinical Scientists. I interviewed them about their routes into the NHS and what their jobs now entail so people could learn more about these career paths. Through this process I actually learnt plenty myself and found it fascinating.

The interviews with the Clinical Scientists (University of Nottingham Alumni) are available on a specific Medical Physics Moodle Page for all the University of Nottingham undergraduates to access in full. We (by which I mean PhD student Molly Rea) have also created “case study” videos which are available on this webpage with edited highlights to emphasize different peoples’ paths into various branches related to medical physics such as radiotherapy, neurophysiology or MRI support. This webpage complements linked pages we have created to give a better understanding of what medical physics is and studying it at Nottingham.

I am already using the interviews with clinical doctors in my teaching in Frontiers in Physics Module. Molly will also be editing these videos so all can see some of the amazing outcomes of the from the use of techniques which originate from medical physics, so watch this space for those!

Finally, I’d just like to thank everyone who agreed to be interviewed by me and the time they gave up to do the videos. I’d also like to thank Molly for editing the interviews and to pull out some of the highlights for everyone to benefit from.

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Guest post by Lom Machin. Lom is a first year Physics BSc student, and they are taking on the challenge of running the 2022 London marathon in partnership with Karnival — the Students’ Union fundraising organisation — and the Meningitis Research Foundation.

Is there a word for excited and terrified in equal measure? Something you’ll lose sleep over but wake with a smile on your face. The extra adrenaline might be useful, because I’m running the 2022 London Marathon! This prestigious 26.2-miler is one of the most famous races in the world, meaning it’s unlikely (1/26 was the last statistic I heard quoted) to get a place through public ballot. Instead, I applied to run for a charity: Meningitis Research Foundation (MRF).

I have never been ‘the sporty one’, likely a common experience for many Physics students. I’ve done years of pantomime and dance, played tenor horn in a brass band, and worked at a bakery, at a chippy, in an ice-cream van and in fast food, but not once had I chosen to run anywhere… until a fateful day I embarked on couch to 5k with a 30-second-run-five-minute-walk foray into the unknown. And then – I was hooked.

When you think of running, your mind probably goes to 100m sprints, to the hated school mile and to the ominous red oval of an athletics track. As it turns out though, when you find your own style, running is an incredible tool for relaxation. Although I’m studying in Nottingham, my home turf is Devon, jogging down country lanes where the only noise is birdsong, the tap-tap-tap of your shoes and the occasional disgruntled birdwatcher if you’re out too early. Even the less objectively peaceful roads of Beeston still provide endless pathways and cut-throughs to explore at my only-just-faster-than-walking, comfortable pace. When I’m running, there’s no room for anything else in my head; lectures to be done, that coursework I might have got wrong, the fact I forgot to buy new batteries for my calculator: when Strava goes on, worries go off. I can’t recommend enough that, at least once, you find a simple route, set off slow, and just see where a jog takes you.

I’m much more a gentle jogger than a ‘proper runner’ per se, so why a marathon? Why take on a challenge that infamously requires so long to train for, while in the midst of settling in to a new routine in a new place?

I say: why not? Everyone finds their peace in different ways, and for me, I know that the excuse and motivation to jog a little further every week will only make me happier. Plus, with the London Marathon pushed back to October, the bulk of my training will be in holidays, rather than clashing with uni work. It is, of course, not just about me. Meningitis and septicaemia can affect anyone anywhere, disproportionately affecting young people (including students) and can kill in hours. Over five million people are affected globally every year, one of whom was my mum. It’s not all bleak – MRF are working with the World Health Organisation to enact a global plan to defeat meningitis by 2030, involving prevention everywhere possible, timely diagnosis, treatment and support. I hope that my efforts can help raise money to save lives, so please check out justgiving.com/fundraising/lomdon for some more info.

What about the marathon itself? All the lab diary work is coming into its own as I create my own ‘marathon lab diary’: kit needed, training plans, energy gels etc. (All of this is out of my own pocket, so every penny donated goes directly to charity.) I’ve got a half-marathon in January to practise my training strategy – I’m mainly hoping to discover that I don’t need to do speed training at all! If you’ve got any questions (or marathon tips!), don’t hesitate to email lomdon2022@outlook.com, and please don’t forget to have a glance at justgiving.com/fundraising/lomdon. Any and all support is so important and appreciated. Now, I’m off for a run!

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Guest post by Grace Gilbert (4th Year MSci Physics student) who recently spent time on a funded summer research placement in the Nottingham astronomy research group working with Prof Alfonso Aragón-Salamanca.  As previously described in this blog, many of our undergraduate students gain valuable resaerch experience by working on summer internships here or abroad. We regularly advertise opportunities in academia and industry, and support students throughout the year with activities advising them on how to apply.  

This summer I completed a placement within the astronomy research group here at the University of Nottingham, focusing on massive galaxy clusters. Most galaxies exist within groups or clusters, with filamentary (threadlike) structures connecting them and feeding more galaxies into them. According to the current model, most large-scale clusters are formed through the merging of smaller clusters; in this case, studying filaments around young clusters like the Virgo cluster (our nearest galaxy cluster) should give us valuable insight into the formation and evolution of massive galaxy clusters.

Figure 1: This image, courtesy of R.B. Andreo, shows the centre of the Virgo cluster. https://apod.nasa.gov/apod/ap150804.html

To study these filaments, they must first be identified and so the aim of my project was to map the galaxies in the Virgo cluster using two different methods for finding the position. The first method used the redshift of a galaxy to find the recessional velocity (the velocity at which a galaxy is moving away from us) and then use Hubble’s law to find a line-of-sight distance. This method was used to find the previously identified filaments but comes with a significant amount of error, as it assumes that the recessional velocity of the galaxy is only due to the expansion of the universe and doesn’t consider the velocity due to galaxies moving around within the cluster.

The second method is a more accurate method and uses the Tully-Fisher relation, which tells us the luminosity of a spiral galaxy if the rotational velocity is known. Because objects further away appear dimmer, a distance can be calculated by comparing this luminosity with how bright a galaxy appears to us on Earth.

My first task was to find and plot all the galaxies and previously found filaments in the Virgo cluster using the redshift distances. I then compared these galaxies with the catalogue of Tully-Fisher distances to find all the galaxies within the Virgo cluster with a known Tully-Fisher distance. Using this information, I then made new plots of the Virgo cluster.

Figure 2: A plot of all the galaxies within the Virgo cluster using the redshift distances. The coloured points represent filaments that had previously been found, the black points show the Tully-Fisher galaxies, and the grey points represent all other galaxies.

My work shows that there are enough galaxies with known Tully-Fisher distances to allow us to map and study filaments around the Virgo cluster and that these Tully-Fisher galaxies do delineate filaments. This could therefore be a viable technique for studying the filaments of galaxy clusters in future research.

This summer studentship has been such a fascinating project and massive learning experience for me – as well as a crash course in galaxy clusters, I’ve learnt how to navigate, query, and use new software to combine information from multiple large databases (a much more difficult task than I expected!). The most valuable thing I’ve gained, however, was the confidence and knowledge to deal independently with a more open-ended project. Although challenging, it taught me to trust myself more, teach myself the tools I needed and how to effectively troubleshoot my own work. It also gave me more space to try things, for some of those things to fail and to learn from that, as well as giving me a taste of research and postgraduate study. I would definitely encourage other undergraduates to take part if they get the opportunity.

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Guest post by Prof Tony Padilla

Every so often our students remind you how wonderfully creative and talented they are.

This couldn’t have been more true as I marked this years assessments for my Advanced Gravity module. Advanced Gravity forms part of MSc in Particles, Gravity and Fields run jointly between Maths and Physics, and is also taken by fourth year mathematical physics students.  It’s a high level course that gets deep into Einstein’s ideas about gravity and relativity.  Anyway, this year I decided to try a new type of assessment. – an outreach task.  I  asked the students to make a video or write a magazine article about a choice of topics related to gravity – stuff like quantum gravity, or black hole information loss, or singularities. And I encouraged them to get creative.

I certainly thought this would be more fun to mark than a problem sheet. I didn’t realise I’d be playing “A night to remember” by Shalamar and dancing on the landing because one student had used it on the soundtrack! Frankly, I was blown away by some of the stuff the students came up with.  I was beyond impressed.  I can’t really do them justice so I think its just  best to share two particularly outstanding contributions. The first is a brilliantly creative written piece describing Penrose and Hawking’s work on singularities; the second a video on the same topic,  which, I must admit,  is far better than the Sixty Symbols video we made after Penrose won his Nobel prize.

Enjoy.

This written piece is by a student  who wishes to remain anonymous:
A conversation between constellations

This video was made by Matt Starbuck (former UoN BSc Physics and Astronomy student, now MSc student Particles, Gravity and Fields):

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Some good news for students who want to develop their careers close to home after graduation. In this guest post by the School of Physics and Astronomy Placement and Partnership Officer Dr. Olga Fernholz, we learn about new funding we are receiving from the Office for Students to support regional opportunities for highly skilled employment.

The School of Physics and Astronomy is part of the White Rose Industrial Physics Academy (WRIPA) which is a partnership between five university physics departments of Hull, Leeds, Nottingham, Sheffield and York plus technical industries from across the regions. WRIPA aims to connect physics students and graduates with the wealth of career opportunities available in the regions and thereby help local technical sectors to innovate and grow. We do that through internships, year in industry placements, final year projects and careers activities among which the annual WRIPA Physics Recruitment Fair is the highlight. WRIPA activities are coordinated at Nottingham by the Placements and Partnership Officer Dr Olga Fernholz.

As part of WRIPA partnership, the School of Physics and Astronomy received Office for Student’s (OfS) Challenge Funding to promote industrial experience for physics students close to home.

Olga Fernholz tells: “We want to link students with the regional and local high-skilled technical jobs, thereby contributing to regional economic development and promoting student’s social mobility. This is done via work placements and industrial projects. It is ultimately up to the students and graduates to decide where they want to study and subsequently work, however, we know that personal choice can be limited and influenced by economic and political factors relating to place. And we know that students who have been exposed to different contexts tend to get better jobs, improving graduate outcomes. Our goal as a consortium is to marry the potential of the place with the potential of the physics graduates.

WRIPA’s five Physics Departments will work as a perfect test site for deploying intra- and extra-curricular interventions. We will boost industrial projects, skills modules, placements, coaching, and so on which will enhance students’ skills and address real needs of the local labour market. This will also subsequently enable students to progress into high-skilled employment in the region or beyond. We have all sorts of ideas about partnerships with employers and events for students, the OfS funding will allow us to cover costs related to industrial experience.”

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This is a long-overdue post to mark the December 2020 graduation of the first cohort from the one-year postgraduate MSc Machine Learning in Science degree offered by the School of Physics and Astronomy. The field of machine learning and artificial intelligence is growing rapidly and these tools are being exploited by researchers in the School of Physics and astronomy working on fields from nanoscience to astronomy.

Students on the MSc are drawn from backgrounds in physics, mathematics, computer science, chemistry, engineering and learn how to apply ML and AI techniques to real scientific problems. This helps build vital skills, enhancing employability in a rapidly expanding area.

And while the first COVID lockdown interfered with the much anticipated final self-driving car challenge, two of our recent MSc graduates, Luke and Sunny, have kindly provided the following reflection on their experience on the course.

Luke Camilleri (BE Electrical and Electronics Engineering, University of Malta 2019):

Machine Learning has been one of the emergent technologies of the past decade. The rapid development of the field has led to impressive technological break throughs in speech recognition such as Siri and Alexa, driverless cars and face recognition the name a few. Studying machine learning has become something of a cliché lately. However, it is an invaluable tool that can be applied to many different fields of study and can help complement scientific research and development. As an electrical & electronics engineer, my interest in how machine learning algorithms could complement more traditional engineering solutions led me to pursue an MSc in Machine Learning in Science at the University of Nottingham.

During this Master’s course I expanded my knowledge of machine learning principles and their underlining statistical foundations. The focus on assignment-based assessment allowed me to develop practical machine learnings skills. It also gave me the breathing room I needed to understand more complex theoretical concepts.

I also had the opportunity to work with Onyx InSight on my dissertation project. Onyx InSight is an engineering company that helps wind turbine operators reduce their operations and maintenance costs through predictive maintenance and the analysis of vibration signals. The goal of my project was to explore the potential of machine learning to detect patterns in vibration data to aid in the automatic detection and classification of various wind turbine component faults without the intervention of human experts. Such a solution would allow for wide range deployment as the inference process could become automated. Working with Onyx InSight exposed me to the challange of working with real world data and developing real world machine learning solutions. Dealing with problems such as data quality, inconsistent labels and large datasets has provided me with invaluable skills that are already serving me well in my career as a machine learning engineer.

Reading for my Machine Learning in Science MSc and studying at the University of Nottingham was a thoroughly enjoyable experience that has helped me develop on a personal and academic level. The skills learnt through the Masters programme have opened up new and exciting opportunities that I hope to develop in my career and further studies.

Sunny Howard (BSc Physics, University of Nottingham 2019):

As the end of my Physics BSc beckoned, I began to ask myself about what I should do next. I had found the computational parts of the degree particularly fulfilling, so I set about finding myself a MSc programme in a relevant field. In this research I discovered machine learning, and the way that it had revolutionized the world. The power of the techniques to solve previously unsolvable problems fascinated me, and I knew that by taking a course in this I would be highly employable. Considering I had already spent 3 years at UoN, I was aware that I am very happy here, and I think one would struggle to find a student who didn’t enjoy their time in Nottingham. These factors caused me to enrol on the Machine Learning in Science MSc. One further thing to mention is that during my BSc I took a module named Symmetry and Action Principles and found that Professor Juan Garrahan was one of the best teachers I had been taught by. The fact that he was acting as one of the course conveners gave me confidence.

The course covers a great deal of content starting from the first principles of machine learning. For me this was essential, as I believe that it is very important to understand the techniques that you are implementing. If you do not, then when they do something unpredictable (which happens often in machine learning), you will have no way of determining the problem. Further to this, a fundamental understanding allows you to develop existing methods as well as creating new ones. We also had the choice of optional modules that covered a wide range of science, allowing us to apply some of the techniques that we had learned. Some of the modules that I chose were Computational Neuroscience and The Physics of Deep Learning. The latter was particularly interesting and allowed me to bridge the gap between my undergraduate and postgraduate courses, by realising the relation between the Ising model (a model of atomic spins in statistical physics) and the Hopfield model (an associative memory model in machine learning).

The degree culminates in a final project, and we were offered several set possibilities but also the option to find a project supervisor from anywhere in the university (if the project was suitable). I took a project with Dr Edward Gillman from the department of Physics, in which we developed a new method for performing reinforcement learning using tensor networks. This project was exactly what I was looking for as it involved creating a new machine learning method, rather than applying an existing one to a scientific problem. The project went well, and we even had enough time to extend the model to multi-agent reinforcement learning.

I am currently a PhD student at the University of Cambridge in the department of Material Science. The aim of my research is to aid the development of memresistive devices, which have the potential to revolutionize random-access memories (RAM) in computers, reducing energy consumption by over 50%. The coding skills that I developed during my MSc have proven essential in the computational projects I have undertaken, involving analytical modelling and density functional theory (DFT) simulations. Although I have not yet used machine learning in my research, I have already noted several opportunities where it may prove useful, such as in finding suitable functionals in DFT. I am highly confident that by the end of my PhD I will have implemented machine learning algorithms.

Congratulations to Sunny and Luke and all the other MSc graduates!

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This is a long-overdue post to congratulate three of our professors from the School of Physics and Astronomy who won major awards from the Institute of Physics. The fact that the awards were announced in October and it’s taken this long to post this gives a small reflection into the workload we’ve been facing this year!  But it’s never too late to recognise when colleagues have done something noteworthy. Congratulations all three.

Congratulations to Professors Gowland, Bowtell and Eaves @uonphysics @UoNScience for scoring a hat-tick of awards from the Institute of Physics @PhysicsNews #WeAreUoN #UoNNews #UoNResearch https://t.co/Ktf6p7ImN7 pic.twitter.com/5I3czfJD8p

— UoN Press Office (@UoNPressOffice) October 29, 2020

Professor Richard Bowtell received the James Joule Medal and Prize for his outstanding application of physics to the innovative development of new hardware and techniques for biomedical imaging, and their application in medicine and neuroscience.  Richard writes:

A common theme of my work has been the manipulation of magnetic fields. In magnetic resonance imaging (MRI) we need accurate control of the of magnetic fields that the water molecules experience. Early in my career I worked on ways of producing controlled magnetic fields that allow images to be created faster and with more information content. Recently it turned out that we could use the same methods inside a magnetically-shielded room to reduce the ambient field to less than 1 nT (50,000 times smaller than the Earth’s field), and this allowed us to make the first wearable magnetoencephalography system (for measuring the tiny  < 1 pT magnetic fields  due to brain activity). In between, I worked on ways of mapping the magnetic properties of brain tissue using MRI, which tell us about the amount of iron that is present (useful because iron content changes in some diseases) and helped to develop new MRI scanners that work at high magnetic field (up to 140000 times larger than the Earth’s field).  Adventures spanning 13 orders of magnitude in the magnetic field were made possible by working with great colleagues, including many Nottingham Physics students, past and present.

Professor Penny Gowland received the Peter Mansfield Medal and Prize for the major contributions she has made in developing novel techiques for quantitative Magnetic Resonance Imaging (MRI) to enable innovative, non-invasive investigations into human anatomy, physiology and biology. She writes:

I was particularly honoured by this award since I came to Nottingham specifically to work with Sir Peter Mansfield, who won the Nobel Prize in Physiology or Medicine in 2003 for his work on the invention of MRI. MRI is a fantastically versatile imaging technique and my research particularly involves expanding the capabilities of the scanners, for instance to allow us to study people moving in upright MRI scanners, and also using MRI to discover new science in the field of human biology. Working with a research fellow, I have recently discovered a new sort of contraction in the placenta and a fourth year undergraduate student has joined us to now use the MRI data try to work out the purpose of these contractions.

Professor Laurence Eaves received the Nevill Mott Medal And Prize for his outstanding contributions to the investigation of fundamental electronic properties of quantum confined systems. He explains:

Laurence Eaves presently studies the way in which electrons carry the electrical current in graphene field effect transistors. Graphene is a unique type of semiconducting crystal made up of a single layer of carbon atoms arranged in a hexagonal crystal lattice. The carbon atoms are held together by strong covalent bonds but are only weakly bonded to any material surface on which they are placed. These weak bonds are called “van der Waals bonds”.

At present, the purest graphene layers are made by exfoliation, in which a single layer of graphene is peeled off from a large crystal of graphite. At Nottingham, Laurence’s colleagues are perfecting ways of growing graphene crystals in ultra-high vacuum and at high temperatures (around 1000 degrees Centigrade) by a technique called molecular beam epitaxy in which carbon atoms are fired at the surface of an insulating crystal such as hexagonal boron nitride or sapphire. Graphene produced by these two quite different techniques provides us with an atomically thin semiconducting layer of very high purity. The figure of merit for electronic quality is the mobility of an electron moving along the graphene layer. This measures the distance an electron can travel without being scattered when a small electrical voltage is applied to the graphene.

By using our superconducting magnets we can make the current-carrying electrons bend into cyclotron orbits whose energies are quantised into well-defined levels, similar to the sharply defined energy levels of electrons in orbit around an atom. This quantisation effect allows us to examine in detail the way in which the electrons are scattered by the thermal vibrations of graphene’s carbon atoms, by residual impurities, and by the side-walls of our transistors. By means of these electrical measurements and by optical spectroscopy on graphene, boron nitride and other atomically thin “van der Waals” crystals, e.g. InSe etc., we hope to optimise these materials for future applications in quantum electronics and opto-electronics.

Congratulations all!

edited 5/4/2021:  added quote from Prof Bowtell.

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