Science and Engineering Summer Research Internships

• Internship duration: 8 weeks
• Supervisor: Debora Belami
• Contact email: debora.belami@manchester.ac.uk

In this hands-on project, you'll be involved with making and testing of membrane electrode assemblies (MEAs)—a critical component of proton exchange membrane (PEM) electrolysers, which split water into hydrogen and oxygen using electricity. Your goal will be to optimise MEA production techniques to develop high-performance, long-lasting assemblies.

You’ll experiment with a variety of fabrication methods, including spray coating, decal transfer, and hot pressing. You’ll also have the chance to modify catalyst ink formulations, investigating how changes in composition affect the efficiency of the MEAs.

To evaluate your results, you’ll use a suite of electrochemical techniques to assess the performance of each MEA and catalyst ink combination. Through this process, you’ll work toward identifying the most effective manufacturing strategy.

This is a fantastic opportunity to gain practical experience in clean energy technology, materials science and electrochemistry, all while contributing to cutting-edge research in green hydrogen production.

• Internship duration: 12 weeks
• Supervisor: Salman Shahid
• Contact email: salman.shahid@manchester.ac.uk

In this project, you’ll design, build, and test a series of short (5 - 7 minute) digital “microlearning” modules to help STEM students better understand assessments and actively engage with course content.

You’ll start by investigating how students currently interact with assessments, identifying gaps in assessment literacy through surveys and focus groups. Based on your findings, you’ll map key concepts - such as rubrics, interpreting feedback, and exam strategies - onto a structured curriculum.

Using intuitive authoring tools like H5P or Articulate Rise, you’ll create a suite of interactive modules covering topics like how to interpret marking criteria, give and receive feedback, and effectively prepare for exams. Once your prototypes are ready, you’ll run a small pilot: recruiting students to test each module, collecting usage analytics, and conducting pre/post surveys to measure changes in their confidence and understanding.

You’ll finish the internship by analysing the data, refining your materials, and producing a toolkit and recommendation report to support broader departmental use.

This desk-based project is ideal if you’re interested in digital learning, user experience, or educational design. No prior research experience is needed—just curiosity, creativity, and a desire to make assessment more accessible and empowering for fellow students.

• Internship duration: 12 weeks
• Supervisor: Salman Shahid
• Contact email: salman.shahid@manchester.ac.uk

In this internship, you’ll co-design, develop, and evaluate a set of digital reflection templates for project-based STEM courses - templates that can also be adapted for use in schools. Using platforms like PebblePad (or a similar tool), you’ll help craft student-centred prompts that encourage your peers to reflect on learning objectives, team dynamics, problem-solving approaches, and how theory connects to real-world challenges.

You’ll organise and run small focus groups to explore how structured reflection supports students in recognising and articulating their skills - especially for CVs and interviews. Based on your findings, you’ll produce a concise staff guide and an onboarding video to support future use of the templates.

By the end of the project, you’ll have developed and tested practical digital tools, gathered evidence of their impact on student feedback literacy and employability, and compiled a shareable case study. Your work will help empower students from all backgrounds to take ownership of their learning journey and build stronger professional identities.

No prior experience with AI or design platforms is needed - just a curiosity for educational innovation and a passion for improving student success.

• Internship duration: 12 weeks
• Supervisor: Salman Shahid
• Contact email: salman.shahid@manchester.ac.uk

In this project, you’ll develop and test a simple AI-powered tool that automatically generates assessment rubrics for STEM assignments. You’ll begin by reviewing existing rubrics and gathering learning objectives from a sample assignment to understand how performance is currently evaluated.

Next, you’ll apply a natural language AI model - such as a Transformer - to generate rubric criteria and define performance levels for each objective. Once your prototype is ready, you’ll conduct a small user test, sharing the AI-generated rubrics with students and staff to gather feedback on clarity, consistency, and usefulness.

By the end of the internship, you’ll deliver a working prototype, a short report on its impact, and a user guide to help others adopt your approach. This project offers a unique opportunity to explore how AI can support fairer and more transparent assessment - no previous AI experience required, just a curiosity for smart technologies and a passion for improving education.

• Internship duration: 8 weeks
• Supervisor: Dr Heather Braid
• Contact email: heather.braid@manchester.ac.uk

In this 8-week internship, you’ll develop a Python-based tool to extract and analyse rock microstructure from 3D X-ray Computed Tomography (XCT) data. Your goal will be to assess subsurface storage potential for critical applications such as carbon capture and storage (CCS), underground hydrogen storage (UHS), and compressed air energy storage.

You’ll gain hands-on experience working with XCT datasets - learning how to preprocess the data and apply image analysis techniques in Python to quantify key properties like porosity and permeability at multiple scales. While existing tools like Avizo provide similar functions, your focus will be on creating reusable, versatile code that can be applied across a variety of datasets to improve accessibility and reproducibility in the field.

This is a great opportunity to develop skills in computational geoscience and data-driven research, while contributing to cutting-edge work in sustainable energy. Your project may even lay the groundwork for a future technical publication.

• Internship duration: 12 weeks
• Supervisor: Perdita Barran
• Contact email: perdita.barran@manchester.ac.uk

In this project, you’ll investigate the structural integrity and authenticity of Semaglutide - a synthetic GLP-1 receptor agonist used to manage Type 2 diabetes and increasingly popular as a weight loss treatment. Semaglutide mimics the naturally occurring incretin hormone, helping to regulate insulin and appetite. However, its recent popularity has led to a rise in unregulated sales through platforms like TikTok, raising concerns about counterfeit and potentially unsafe formulations.

You’ll explore how ion mobility mass spectrometry (IM-MS) can be used to analyse the gas-phase structure of Semaglutide and detect possible impurities or modifications that occur during storage, handling, or manufacturing. Because Semaglutide is a peptide-based drug with high conformational flexibility, IM-MS is an ideal tool for probing its structural landscape and understanding how these conformations affect its pharmacokinetics and therapeutic effectiveness.

By comparing verified pharmaceutical samples to those acquired from rogue traders on TikTok, you'll contribute to efforts aimed at identifying adulterated or counterfeit versions. Your findings could help shape more robust quality control methods for this increasingly important drug.

This internship offers a valuable opportunity to apply advanced analytical techniques in a real-world public health context while gaining experience at the intersection of pharmaceutical science and chemical analysis.

• Internship duration: 12 weeks
• Supervisor: Prof Fred Currell
• Contact email: frederick.currell@manchester.ac.uk

In this internship, you’ll join a dynamic, multidisciplinary team - including academic staff, a national lab secondee, and fellow student researchers - working together at The University of Manchester’s main campus. After an intensive one-week training workshop, you’ll dive into research using MIRaCLE (Manchester Inhomogeneous Radiation Chemistry by Linear Expansion), a cutting-edge, in-house software package for modelling and optimising personalised nuclear medicine nanoparticles.

Your role will be part of the Optimised Production of Theragnostic Isotopes of Copper and Scandium (OPTICS) project. You’ll work with nanoparticles that contain either copper or scandium isotopes - specifically, scandium in its carbonate form. At the Dalton Cumbrian Facility (DCF), our automated systems handle isotope transmutation, separation, and synthesis, eliminating the need for direct human intervention.

During the internship, you'll help push the boundaries of this work as we explore new collaborations with industry partners like Urenco to expand our nanoparticle formulations to include titanium and zinc isotopes.

So far, the team has simulated physical dose distributions around these nanoparticles. Your contribution will focus on modelling the generation of genotoxic species - like hydroxyl radicals - that are believed to play a crucial role in the therapeutic effects of this approach. This phase of the project is key to demonstrating the full clinical potential of our theragnostic technology.

This is an exciting opportunity to gain experience in computational chemistry, radiopharmaceutical development, and collaborative research at the frontier of nuclear medicine.

• Internship duration: 12 weeks
• Supervisor: Jonathan Skelton
• Contact email: jonathan.skelton@manchester.ac.uk

In this project, you’ll contribute to improving the usability and reach of an open-source code for simulating vibrational spectra - including infrared (IR), terahertz, and Raman simulations. These simulations are widely used to validate experimental and computational methods in materials science, but many existing tools don’t support advanced experiments like polarised Raman on single crystals.

You’ll start by identifying a selection of relevant experimental studies from the scientific literature. Then, you’ll use the simulation software to reproduce the measurements, generating reference input/output files and creating step-by-step tutorials that showcase the tool’s capabilities for other users.

Throughout the project, you’ll gain valuable experience in solid-state modelling with density-functional theory (DFT), Python programming, technical documentation, and collaborative software development using GitHub. These are all highly transferable skills for both academic and industry-focused research careers.

This is a great opportunity to strengthen your understanding of materials modelling while contributing to a widely used scientific software tool.

• Internship duration: 12 weeks
• Supervisor: Igor Larrosa
• Contact email: igor.larrosa@manchester.ac.uk

As part of our continued efforts to push the boundaries of organic chemistry and catalysis to facilitate and simplify the synthesis of complex molecules and pharmaceuticals, the Larrosa group has recently developed a novel, highly reactive ruthenium (pre)catalyst. This precatalyst has proven to be robust in driving a wide array of chemical reactions while still being air- and moisture-stable, which boosts both its versatility and the applicability of the chemistry that is developed with it. For these reasons, and due to the relative recentness of the discovery, we are thoroughly interested in overcoming previous limitations in the field by exploring new chemical reactions with our precatalyst.

As an intern, you’ll have the chance to immerse yourself in a chemistry research lab, gaining hands-on experience with planning, setting up, and analysing novel chemical reactions. You’ll work with advanced instrumentation and modern research techniques, building practical skills that will support your growth as a scientist. This is a unique opportunity to explore the dynamic world of chemical research, deepen your understanding of catalysis, and take meaningful steps toward a future in scientific discovery.

• Internship duration: 12 weeks
• Supervisor: Ashok Keerthi
• Contact email: ashok.keerthi@manchester.ac.uk

In this internship, you’ll explore novel and unconventional two-dimensional (2D) materials—specifically covalent organic frameworks (2D-COFs)—and investigate their chemical stability and properties. You’ll take part in designing and synthesizing 2D polymers and 2D-COFs, then work with the resulting layered materials to characterise them using optical microscopy and atomic force microscopy.

You’ll focus on understanding how these ultrathin layers—ranging from a single atom to a few nanometres thick—behave under ambient conditions. You’ll analyse changes in their size, thickness, and optical contrast over time, and contribute to building stability profiles for different exfoliated 2D-COFs.

In addition, you’ll study how the size and thickness of these materials influence their photophysical properties, and use FT-IR spectroscopy to investigate their chemical structure and composition. Your findings will contribute to a broader research goal: developing advanced materials for important applications like molecular sieving and carbon capture.

This is an excellent opportunity to gain hands-on lab experience at the intersection of materials chemistry and nanotechnology, while contributing to cutting-edge research on next-generation sustainable materials.

• Internship duration: 12 weeks
• Supervisor: Edward Jones
• Contact email: edward.jones-3@manchester.ac.uk

Explore the frontier of neuromorphic engineering by developing intelligent software models inspired by brain cells.

In this project, you’ll write and train neuromorphic models to choose the best 'next move' in a simple turn-based game.

You will learn:

• What neuromorphic software models (spiking neural networks [SNNs]) are
• How to implement SNNs in software
• How SNNs differ from more conventional machine learning methods
• How games can be represented in software

We will address the following research questions:

• How can neuromorphic software models learn to recognise good moves in a simple turn-based game (noughts and crosses)?
• Can this approach to game-playing be developed and applied to more complex games and other tasks?
• Which games, if any, are a good proxy for real-world tasks? What are the strengths and weaknesses of the developed neuromorphic approach?

• Internship duration: 12 weeks
• Supervisor: Chamil Abeykoon
• Contact email: chamil.abeykoon@manchester.ac.uk

Compared to active cooling methods, passive cooling often offers a cost-effective, easy-to-install and energy-saving solution without significant changes to the design complexity. Recent research has focused on improving PCM cooling systems for temperature management in battery modules or packs. In this project, you will support  a comprehensive study to understand the current state-of-the-art in the related area with a literature review. Then, you’ll identify potential techniques for future EV battery cooling applications, and you’ll have the opportunity to participate in light experimental activities with a PHDA/PhD student.

• Internship duration: 12 weeks
• Supervisor: Leandro Maio
• Contact email: leandro.maio@manchester.ac.uk

This study aims to introduce a new type of directional transducer designed for structural health monitoring (SHM) applications based on ultrasonic guided waves (GWs). GWs are extensively used for SHM due several useful properties, such as:

1. the ability to travel long distances with little attenuation, which makes them suitable to inspect inaccessible or hard-to-reach spaces such as piping systems and fuel tanks;
2. the ability to detect both inner defects (e.g., delaminations) and surface flaws (e.g., corrosion, cracks).

In this regard, the traditional use of conventional piezoelectric transducers (PZTs), e.g., phased array systems, is hampered by several problems, including complex hardware systems, high power consumption, and high integration costs. Drastic hardware simplification and cost reduction of GW-based systems can be achieved by using transducers that present inherent directional capabilities, when generating and sensing elastic waves, exhibiting preferential radiation/sensing directions.

To overcome such limitations, a steerable acoustic transducer (SAT) is here proposed to take advantage of built-in directional properties. It is based on the principle that the maximum transducer response is determined by the intersection between the medium’s dispersion relation and the wavenumber representation of the device electrode’s shape. Therefore, the distribution of electrode’s material in the wavenumber domain leads to a sensor which is characterized by a unique relationship between frequency and direction of wave generation and sensing.

By exploiting the frequency-dependent spatial filtering effect, such a transducer can generate/receive waves in a specific direction corresponding to the frequency content of the transmitted/received signals, thus simplifying the hardware requirements.

Few promising studies are available in the literature on this type of transducer applied just to isotropic structures, by opening to new investigations and in-depth analyses. Therefore, the proposal goal is to carry out a feasibility study of SAT for SHM applications. For the purpose, Python scripts and Finite Element (FE) simulations will be developed to evaluate the frequency-dependent unidirectionality of the proposed device and consequently the transducer performance.

Finally, the possibility to adopt additive manufacturing for the transducer production will be also analysed for paving the way to future developments.

• Internship duration: 12 weeks
• Supervisor: Dr Jane Wood
• Contact email: jane.wood-2@manchester.ac.uk

During this 12-week internship, you’ll explore how different dyeing processes influence the release of microfibres during textile laundering, with a particular focus on comparing natural and synthetic dyes. Microfibre pollution from domestic washing is an increasing environmental concern - especially in textiles dyed with conventional synthetic (chemical) dyes. In this project, you’ll investigate whether using natural, plant-based dyes can change fabric properties in a way that reduces microfibre shedding, and how these changes affect the colour and quality of the dyed material.

You’ll conduct lab-based dyeing trials using selected natural dye sources and compare them with industry-standard synthetic dyes, applying both to a variety of textiles. Once dyed, the fabrics will undergo standardised laundering tests. You’ll quantify microfibre release using filtration and microscopy techniques developed by researchers at The University of Manchester, and analyse the results to assess how fibre loss varies between dye types and what that means for environmental sustainability.

This internship gives you hands-on experience in sustainable textile processing, lab-based testing, and environmental impact assessment - allowing you to contribute directly to innovative research aimed at reducing the ecological footprint of textile dyeing and finishing.

• Internship duration: 8 weeks
• Supervisor: Matthias Heil
• Contact email: m.heil@maths.manchester.ac.uk

In this internship, you’ll explore the stability of deformable plates—structures that are central to a wide range of natural and engineered systems. These include the rooftops of sports arenas and airports, unfolding solar panels in space, floating leaves of water lilies, and sedimenting microplastic particles, which often take the form of thin sheets. While much of the existing research has focused on the critical thresholds at which these structures buckle (lose their original shape in response to excessive forcing), this project invites you to study the diverse ways in which deformation continues to evolve after buckling occurs.

Mechanical instabilities in slender structures don’t just present challenges—they also create opportunities for scalable, reversible, and robust functionality. Think of the Touch-Me-Not plant, whose leaves fold and close repeatedly in response to touch: a natural example of reversible buckling. To harness such behaviour in applications, we first need a predictive understanding of how these structures deform.

In this project, you’ll study simply supported plates that are free to deform under their own weight. You’ll carry out experiments using rectangular elastic sheets of varying sizes. The hypothesis is that beyond a certain size threshold, the post-buckling shape transitions from an arch to a drape. Your aim will be to explore these post-buckling forms and analyse how they relate to the geometry of the sheet.

This internship gives you hands-on experience in experimental mechanics and the opportunity to contribute to fundamental research on structural stability and deformation.

• Internship duration: 8 weeks
• Supervisor: Dr. Abdalla M. Omar
• Contact email: abdalla.omar@manchester.ac.uk

In this internship, you’ll explore how deployable scaffolds which are devices that change shape inside the body, can be developed to encapsulate tumours and deliver targeted, localised cancer treatment. Graphene-enhanced polymers, known for their exceptional mechanical, electrical, and functional properties, offer exciting possibilities in this area when combined with emerging 3D and 4D printing techniques.

You’ll investigate how multi-material 4D printing enables the fabrication of shape-morphing polymers such as PETG enhanced with graphene. Your research will focus on how printed architectures can be manipulated in response to electrical stimuli. To characterise these materials, you’ll carry out a range of analyses, including X-ray diffraction (XRD), differential scanning calorimetry (DSC), and shape-morphing tests. These investigations will help determine how material composition and process parameters influence shape transformation behaviours relevant to biomedical applications.

Working under the guidance of your supervisors, you’ll collaborate with researchers in the 3D printing lab at the interface of materials science, advanced manufacturing, and biomedical engineering. As part of your experience, you’ll also receive support in drafting an article for the Journal of Undergraduate Research, allowing you to reflect on your findings and develop essential scientific writing skills.

This internship offers a unique opportunity to gain hands-on experience in a cutting-edge research environment, preparing you for further study and a potential future in interdisciplinary PhD research.

• Internship duration: 8 weeks
• Supervisor: Holly Perrett
• Contact email: holly.perrett@manchester.ac.uk

Ion beams are widely used in nuclear physics, chemistry, and industry. This project focuses on resonance ionisation spectroscopy, which uses finely-tuned lasers to excite species with specific electron configurations. Isotope selection is achieved by passing ions through a magnetic field; different mass-to-charge ratios (m/q) produce distinct trajectories, enabling separation.

You’ll work with an existing beamline setup, where a magnetic mass separator is being developed. A test rig using an electron cyclotron resonance (ECR) ion source continuously ionises injected gas. Ions are directed through a curved electromagnet, with the field strength scanned to transmit selected m/q values.

As part of the project, you'll conduct experiments to characterise the ion source and separator, and contribute to the design linking the separator to the beamline. You'll also gain experience with vacuum systems, ion optics, and simulation software for modelling ion trajectories.

• Internship duration: 8 weeks
• Supervisor: Ziwei Wang
• Contact email: ziwei.wang@manchester.ac.uk

In this internship, you’ll explore cutting-edge research at the intersection of materials science and neuromorphic computing - an emerging field aiming to revolutionise how machine learning (ML) and artificial intelligence (AI) are powered. While conventional AI systems use neural networks trained on GPUs and TPUs based on the traditional Von Neumann architecture, they rely on extensive data transfer between memory and processors, resulting in high energy consumption.

Neuromorphic computing offers a more energy-efficient alternative by integrating memory and computation within a single device. At the heart of this approach are memristors - electronic components with multiple resistance states—making them ideal building blocks for brain-inspired hardware.

In this project, you’ll investigate the tunable resistance of two-dimensional vanadium pentoxide (V₂O₅), a material previously shown to act as a bipolar memristor due to oxygen-vacancy migration at elevated temperatures. Although this behaviour has been observed, the ability to control it reliably remains a challenge. Your aim will be to develop a method for selectively programming the resistance of V₂O₅, advancing its potential use in neuromorphic computing devices.

You’ll gain hands-on experience in experimental materials research and device characterisation, contributing to the development of low-power, high-efficiency alternatives to current AI hardware. This project is ideal if you’re interested in the future of electronics, energy-conscious computing, and next-generation AI technologies.

• Internship duration: 12 weeks
• Supervisor: Ingo Dierking
• Contact email: ingo.dierking@manchester.ac.uk

In this internship, you’ll investigate frustrated liquid crystals - materials that exist only within narrow temperature ranges, such as the Blue Phase and the Twist Grain Boundary (TGB) phase. These unique phases emerge from a competition between molecular chirality and structural incompatibility, resolving this frustration through the formation of defects.

Your project will focus on using machine learning to identify and classify these complex phases. You’ll begin by generating a large texture dataset, comprising several thousand images captured through video acquisition. This image bank will serve as the training and testing dataset for machine learning models such as convolutional neural networks (CNNs) and inception models, used for automated phase identification.

You’ll work on optimising these algorithms to improve accuracy and minimise loss, blending experimental data collection with computational analysis. This internship is ideal if you’re interested in soft matter physics, computer vision, or the intersection of materials science and artificial intelligence.

By the end of the project, you’ll gain valuable experience in both experimental techniques and machine learning model development - skills that are highly sought after in research and industry alike.

• Internship duration: 8 weeks
• Supervisor: Laura Wolz
• Contact email: laura.wolz@manchester.ac.uk

In this project, you’ll work with radio observational data collected by the South African MeerKAT telescope, a precursor to the Square Kilometre Array (SKA) Observatory. As part of the MeerKLASS survey, MeerKAT scans the sky at frequencies between 500–1000 MHz to map the large-scale structure of the Universe by detecting emission from cold hydrogen gas.

One of the major challenges to this science goal is contamination from terrestrial radio signals - known as radio frequency interference (RFI). The standard approach is to flag (i.e. remove) data points heavily affected by RFI, but this can lead to significant data loss. In this project, you’ll use the latest MeerKAT observations to investigate the RFI environment in the South African desert. Your aim will be to map RFI in local coordinates and over time to help identify and characterise sources of contamination.

MeerKAT consists of 64 dishes and collects data across 2048 frequency channels in two polarisations - making this a true Big Data project. You’ll explore efficient methods for compressing and visualising the data, so your RFI analysis can contribute to improved data processing workflows and inform future observing strategies.

This internship offers you experience at the frontier of astrophysical research, where you'll combine observational data analysis, signal processing, and visualisation techniques to tackle real-world challenges in radio astronomy.

• Internship duration: 8 weeks
• Supervisor: Michel De Cian
• Contact Email: michel.decian@manchester.ac.uk

The LHCb experiment at the LHC is running with an all-software trigger system since 2022, recording decays of particles at an unprecedented rate and allowing for precise measurements of rare disintegrations.

In this project, we will analyze the first data from the 2025 data taking campaign and study a particle called Bs meson and its decay into specific final states. Using frameworks to analyze large amounts of data, we will investigate the decay spectrum, attempt to confirm so-far hypothesized resonance states in the spectrum, and further our understanding towards an extraction of the CKM-matrix element Vcb, a fundamental constant of nature.

• Internship duration: 12 weeks
• Supervisor: Sam Azadi
• Contact email: sam.azadi@manchester.ac.uk

In this project, you’ll investigate the fundamental properties of the two-dimensional electron liquid (2D UEL)—a simplified yet powerful model used to understand the behaviour of interacting electrons confined to two dimensions. The 2D UEL plays a central role in modern condensed matter physics, forming the basis for understanding systems such as semiconductor heterostructures, two-dimensional materials like graphene, and quantum Hall devices.

Your work will focus on analysing the ground-state properties of the 2D electron liquid at zero temperature. You’ll examine how key physical quantities—including the Fermi energy, kinetic energy, exchange energy, and compressibility—depend on electron density, described by the Wigner–Seitz radius (rₛ).

You’ll combine analytical derivations with numerical modelling in Python, giving you the chance to explore quantum many-body effects in low-dimensional systems through both theory and computation. By the end of the project, you’ll have developed a solid conceptual understanding of interacting electron systems in two dimensions, along with practical computational skills that are widely applicable in theoretical and computational physics.

• Internship duration: 12 weeks
• Supervisor: Finn Box
• Contact email: finn.box@manchester.ac.uk

In this project, you’ll explore a new method for measuring the surface tension of droplets—an essential property in fluid dynamics and materials science. Traditional methods, such as droplet shape analysis or the Wilhelmy plate technique, are well established but require relatively large liquid volumes (typically 10 µL or more). Recently, researchers in the Physics of Fluids group have developed an innovative approach that uses pressure measurements to determine surface tension, working with droplet volumes as small as ~1 µL.

Your role will be to benchmark this method using a variety of liquids and determine the smallest droplet volume for which it remains accurate and reliable. You’ll measure the dynamic surface tension of sessile droplets as they evaporate, allowing you to investigate how surface tension evolves as volume decreases to sub-micron levels.

Through this work, you’ll contribute to advancing the technology readiness level (TRL) of the device from proof-of-concept (TRL 3) to validation in a relevant environment (TRL 5). Your findings will support future discussions with the University’s Innovation Factory and potential industrial collaborators, such as KRÜSS, on the development and licensing of intellectual property.

You’ll carry out these experiments using specialised equipment in the Physics of Fluids and Soft Matter laboratories - gaining valuable experience in experimental fluid mechanics, precision measurement techniques, and applied research translation.

• Internship duration: 8 weeks
• Supervisor: Prof. Ian Browne
• Contact email: ian.browne@manchester.ac.uk

After decades of discoveries in radio astronomy, could there still be something surprising to uncover? Possibly! The American balloon-borne experiment ARCADE-2 has reported the detection of an unexplained radio background emission that becomes visible only at low radio frequencies. The origin of this signal is hotly debated—is it a new, previously unknown cosmological background, or an unmodelled component of the Galaxy’s own radio emission? (See Singal et al., 2018 for a review.)

In this project, you’ll help investigate this question using L-BASS, a special-purpose radio telescope located at Jodrell Bank Observatory. L-BASS is designed specifically to test the ARCADE-2 claim by creating a precisely calibrated map of the sky around 1.42 GHz (L-Band). The telescope features two 3-metre-long horn antennas and is now fully operational.

Your role will focus on refining the calibration of the instrument and assisting in the collection and analysis of data to produce some of the first accurate measurements of sky brightness at this frequency. You’ll gain hands-on experience with observational radio astronomy and instrument calibration, working as part of a small, active research group.

Most of the work will take place on-site at Jodrell Bank Observatory. You’ll travel by train to Chelford station, where a member of the L-BASS team will collect you and bring you to the observatory.

This is a unique opportunity to engage directly with frontier research in astrophysics while building practical skills in radio astronomy instrumentation and data analysis.