The UROP is designed to support undergraduates studying at the University of Cambridge who are going to return for at least one more year of undergraduate study.

Final year undergraduates and postgraduate students should not apply.

Some projects with external funding have additional restrictions, such as those funded by EPSRC.

If you have any questions please contact Joe Goddard, Industrial Placements Coordinator, who administers UROP projects for the Department of Engineering.

Further information can be found below:

• Student click here.
• Staff click here.

Available Projects

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Boron-free Small Modular Reactor fuel design and evaluation 

Supervisor Details

Dr Paul Cosgrove, Department of Engineering, pmc55@cam.ac.uk 

Project Description  

Context: The Rolls-Royce Small Modular Reactor (SMR) is a pressurised water reactor (PWR). It is unconventional in the sense that it controls the reactor’s criticality using only control rods; most commercial PWRs use soluble boron. Soluble boron is often used because it depresses the fission rate uniformly across the core, as opposed to inserting control rods which depress the fission rate locally. For a reactor operating at constant power, a local fission rate depression implies that the same power is being generated from a smaller volume. This increase in power density can result in thermal safety limits being breached, e.g., local fuel melting or cladding damage. 

The most obvious way in which this challenge can be overcome is to ‘zone’ the fuel: along the fuel assembly’s height, one could insert fuel pellets of different enrichments or featuring embedded neutron absorbers. This is commonly done in Boiling Water Reactors, for example. The resulting fuel would be more complicated and expensive, but this would allow the core to safely operate at higher powers. 

Project aim: This project proposes to optimise a PWR fuel assembly by varying the composition of the fuel axially. The project would evaluate the extent of power peaking and thermal margins through the fuel’s useful life, i.e., as it burns up. Using common engineering approximations, this analysis would be extrapolated from a single fuel assembly to the whole reactor core, allowing the behaviour of the resulting power plant to be evaluated. A good fuel would achieve a long life, at low cost, with large thermal margins, allowing a more compact (and therefore cheaper) reactor to be designed. 

Students who take the project would develop the following skills: 

• Nuclear reactor design logic 
• Monte Carlo methods applied to neutron transport 
• Optimization techniques, such as genetic algorithms and surrogate modelling 
• Python coding to co-ordinate creating different reactor geometries and evaluating them with respect to different design constraints 

If the project went exceedingly well, there is scope to perform a preliminary economic analysis of the Rolls-Royce reactor when using more aggressively designed fuel, exposing students to some commonly used financial tools in nuclear engineering. 

These skills span a range of useful disciplines, from numerical methods, to engineering design, to financial modelling. A successful student would be a strong candidate for employment in the nuclear field (including fusion where the physics and analysis techniques are very similar). Monte Carlo methods and optimization algorithms are common throughout engineering, physics and finance and so the skills developed are likely to benefit students across a range of possible future careers. 

Essential Skills and Attributes  

• Should be reasonably competent at coding/scripting, i.e., basic python abilities. Should have an interest in nuclear energy. 

Desirable Skills

Knowledge of nuclear reactor design and operation. Knowledge of nuclear reactor physics and thermal-hydraulics. 

Timing 

Flexible 

Supporting Information 

https://arxiv.org/pdf/2601.20079

A prior economic analysis of various small modular reactors: www.sciencedirect.com/science/article/pii/S0029549321004404 ">https://www.sciencedirect.com/science/article/pii/S0029549321004404 

*Description of how you will advertise for and select candidates based on merit and encourage applications from beyond Cambridge 

Application Details

Please email Dr Paul Cosgrove, pmc55@cam.ac.uk with a copy of your CV along with a short statement in your email explaining why you are interested in this particular project.

Deadline: 1st May. 

 

Thermodynamically Consistent Wave-Speed Averaging in Non-Ideal Multiphase Flow Simulation

 

Primary Supervisor Details

Prof. Andrew Wheeler, Department of Engineering, aw329@cam.ac.uk

Co-Supervisor

Dr Katharina Tegethoff, Department of Engineering, kt568@cam.ac.uk

Project Description

High-fidelity simulation of compressible flows involving non-ideal thermodynamics and phase change is becoming increasingly important in advanced engineering applications, including low-carbon power cycles and next-generation propulsion systems. In such regimes, strong thermodynamic nonlinearities influence wave speeds, compressibility, and numerical stability in ways that differ fundamentally from classical ideal-gas behaviour.

An in-house finite-volume solver has been developed to model multicomponent, multiphase, non-ideal fluid flows. During the development of a Roe-type flux formulation capable of handling such flows, an intriguing numerical phenomenon was observed: two seemingly equivalent definitions of the Roe-averaged speed of sound led to markedly different stability behaviour. When the wave speed was evaluated from a consistently averaged thermodynamic state, the scheme remained robust. However, directly averaging the speed of sound from thermodynamically consistent left and right states – closer in spirit to Roe’s original formulation – introduced numerical oscillations and degraded convergence.

This discrepancy points to a deeper issue. Approximate Riemann solvers assume that the averaged state used to define wave speeds corresponds to a thermodynamically admissible configuration consistent with the flux linearisation. In non-ideal and multiphase-capable models, where sound speed and compressibility depend nonlinearly on the equation of state and may vary sharply, directly averaging a derived quantity such as the speed of sound may destroy that consistency. The resulting mismatch can alter the eigenstructure of the flux Jacobian, affect dissipation levels, and compromise stability. Since wave speeds directly control numerical dissipation in high-resolution schemes, such inconsistencies may also influence the behaviour and design of flux limiters in non-ideal flow regimes.

This project will analyse this mechanism in a controlled setting. A reduced two-dimensional research testbed will be constructed to implement and compare alternative Roe-averaging strategies. The student will quantify thermodynamic consistency errors, examine their impact on wave speeds and flux differences, and relate these to the emergence of oscillations. The objective is to determine why one averaging strategy preserves stability while the other does not, and to formulate clear criteria for wave-speed evaluation in non-ideal compressible flow.

The results will strengthen the theoretical basis of flux construction for high-fidelity multiphase simulations and support more reliable predictive modelling in advanced energy and propulsion systems. The project offers a focused research problem at the interface of computational fluid dynamics and nonlinear thermodynamics, with well-defined analytical and numerical milestones suitable for an eight-week UROP.

Essential Knowledge, Skills, and Attributes

• Good programming skills in at least one language (e.g. Python, C/C++ or similar)
• Solid understanding of core fluid mechanics concepts from undergraduate courses
• Ability to think logically about mathematical expressions and translate them into code
• Motivation to work independently on a well-defined research problem

Desirable Skills

• Strong interest in computational fluid dynamics and numerical modelling
• Curiosity about how mathematical models translate into working engineering tools
• Interest in the interaction between thermodynamics and fluid mechanics

Timing 

The project will run for eight consecutive weeks during the summer, with start dates flexible to accommodate the student’s availability.

Application Details

Please email Dr Katharina Tegethoff, Department of Engineering, kt568@cam.ac.uk, with a copy of your CV along with a short statement in your email explaining why you are interested in this particular project.

Deadline for applications: 17 April 2026

 

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Statistical modelling of Virtual reality (VR) headset orientation for optical wireless communication applications

Dr Iman Tavakkolnia – Department of Engineering – Electrical Engineering Division – it360@cam.ac.uk 

Project Description

This summer internship project will extend previous work on statistical modelling of Virtual reality (VR) headset orientation for optical wireless communication applications – performed as a 4th-year project. The prior study developed and validated a stochastic model of yaw, pitch, and roll for a stationary VR user, embedded these distributions into a Monte Carlo optical channel simulation, and quantified the impact of orientation randomness on the performance of an optical wireless communication (OWC) link. The aim of this internship is to advance the model toward realistic, movement-rich VR scenarios and to validate key findings experimentally in the laboratory. The work will directly contribute to the research at the LiFi Research and Development Centre and will be dissiminated through our channels in TITAN Telecoms Hub www.titancambridge.com/">https://www.titancambridge.com/

The project will have two main objectives:

1) Extend Orientation Modelling to Dynamic Movement Types

The student will design and conduct new data collection experiments capturing head orientation during different VR use cases, including:

• Seated interactive gaming (moderate head motion),
• Standing or room-scale experiences (large rotations and mild translation),
• Rapid head-turn scenarios (e.g., reaction-based games).

Using a Unity-based logging application (building on the previous framework), the student will collect time-stamped yaw, pitch, roll (and optionally position) data. The analysis will move beyond independent, static distributions and investigate:

• Temporal correlation of orientation (e.g., autoregressive models),
• Joint distributions between axes,
• Scenario-dependent statistical models,
• Movement classification (stationary vs dynamic modes).

This will result in refined stochastic models suitable for realistic VR traffic and mobility conditions.

2) Laboratory Measurement and Validation (depending on the progress on objective 1)

A key advancement over the previous project is experimental validation. The student will build a small-scale optical wireless testbed and reproduce selected simulation scenarios in the lab and compare:

• Measured signal variation under real head motion,
• BER or SNR degradation versus prediction,
• Validity of statistical models under dynamic conditions.

This will provide the first measurement-backed validation of orientation-aware LiFi modelling for VR.

By the end of the internship, the project will deliver: (i) scenario-dependent stochastic orientation models, (ii) simulation results for dynamic VR use cases, and (iii) experimental validation of motion-induced channel variation. The outcomes will directly inform the design of robust, orientation-resilient LiFi systems for immersive applications.

Essential Knowledge, Skills, and Attributes

• Strong foundation in mathematics (probability, statistics, linear algebra).
• Experience with Python 
• Basic knowledge of signal processing concepts 
• Understanding of electromagnetics or wireless communication fundamentals.
• Ability to analyse experimental data and interpret results critically.
• Comfortable working with laboratory equipment (oscilloscope, power supplies).
• Good technical writing skills.
• Independent problem-solving ability and structured working style.

Desirable skills

• Prior exposure to optical wireless communications, LiFi, or VLC.
• Experience with VR development.
• Experience with photodiodes, LEDs, or optoelectronic hardware.
• Background in communication systems (modulation, coding, OFDM).
• Experience building experimental setups and debugging hardware.

Timing 

8 weeks flexible over summer.

Application Details

Please email Dr Iman Tavakkolnia, it360@cam.ac.uk, with a copy of your CV along with a short statement in your email explaining why you are interested in this particular project.

Deadline for applications: 30th April 2026.

 

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Aviation Impact Accelerator (AIA)

Primary Supervisor Details

Professor Rob Miller, rjm76@cam.ac.uk

Co-Supervisors / Industrial Collaborators 

Samuel Gabra, Whittle Laboratory, sg788@cam.ac.uk

Project Description 

Out of a series of roundtables hosted by HRH the Prince of Wales, the Aviation Impact Accelerator (AIA) emerged to accelerate the transition to net-zero flight. The AIA draws on a broad international portfolio of academic and industrial expertise to build a whole system simulator that can map pathways and accelerate the journey to climate neutral aviation by means of interactive, evidence-based models. 

First-order models developed by the AIA have identified that several aviation technologies have the potential to replace existing jet fuel aircraft in the future pending technological development, industrial investments and new policy frameworks. However, the uncertainty surrounding these areas, together with the inherent interdependence complexity of the AIA’s “system of systems” approach, represent significant challenges to the development of our model. In order to improve the AIA’s whole system model, it is necessary to: (i) increase the resolution of its constituent models, and (ii) expand the range of conducted analyses to better encompass the complexity of the problem. 

To this end, four UROP projects are offered, with focus on: fuel, fleet, and contrails. We are looking for students who have just finished their 2nd or 3rd year, with skills in one or multiple areas, including aerospace, chemical and electrical engineering, economics and system modelling as well as solid software skills, preferably in Python, alongside proficiency in modelling and data analysis. The specific research question to be addressed during the UROP will be refined based on the interests and skills of applicants, as well as on the status of the AIA models over the summer.

The UROP projects will run for 8-10 weeks.

The AIA partners with a wide range of key industrial, business and academic players, among which are Boeing, Rolls Royce, MIT to name a few. Joining the AIA will give you the chance to work in a collaborative environment, interact with influential stakeholders and most importantly, ‘accelerate’ your positive impact on the world!

For more information about the AIA, Check our www.aiazero.org/">http://www.aiazero.org/">webpage.

If you would like to apply, please send your CV and a short cover letter to aia-ops@eng.cam.ac.uk. 

Deadline for application: 8th May, 2026.

 

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Piezoelectric-Robot Control Software for High-Throughput 2D Material Discovery 

Prof Stephan Hofmann, Engineering, sh315 

The unique chemical, mechanical, and optoelectronic properties of two-dimensional (2D) materials offer innovation opportunities in fields ranging from electronics to sensing. Thousands of 2D materials have been predicted, but few new materials are realised, and hardly any can be manufactured at industrial scales with sufficient control. Current lab-scale fabrication equipment is orders of magnitude larger than necessary, slowing 2D material discovery to months if not years. The aim of this UROP project is to develop closed-loop control software for piezoelectric robotic arms, facilitating a new, radically miniaturised platform to make and manipulate atomically thin 2D materials inside a scanning electron microscope (SEM). 

Figure 1: a) Overview of miniaturised Hofmann Group in-SEM fabrication system. b) Project aim is closed-loop control of this robotic arm to facilitate c) high-throughput studies of 2D-material growth. d) Robot control would be an add-on to the current custom SEM control software (Python-based). 

The Hofmann Group https://hofmann-group.eng.cam.ac.uk/publications/">currently grows atomically thin materials in an SEM using precursor chemicals introduced through a quartz capillary that is held by a robotic arm (Figure 1a). Electron microscopes rely on electrons rather than photons for imaging, enabling far higher imaging resolutions than in optical microscopy. 2D material features as small as 20 nm may, therefore, be resolved and studied in real-time, providing valuable information about the type and rate of material growth under different fabrication conditions. The broader research aim beyond the UROP project is to expedite these studies by augmenting custom software (Figure 1d) to control a piezoelectric robot arm (Figure 1b). Material could then be grown at different points on a substrate, changing the fabrication conditions between each experiment (Figure 1c). 

Over eight weeks, the candidate will: 

• Familiarise themself with the Pythonic control of the robot arm and using the SEM
• Add a feature to the custom SEM graphical user interface (GUI) to store a template image of the precursor-supply capillary
• Use image template matching to track the capillary as the robot arm moves 
• Determine when the capillary has contacted the substrate by monitoring for sudden changes in the capillary position as the substrate is raised (via motion-stage)
• Use inverse kinematics to move the capillary parallel to the substrate in a pattern conducive to the high-throughput experiment platform depicted in Figure 1c
• Be fully embedded in state-of-the art research (work likely to lead to journal publication)  
• Learn about ongoing projects across West Cambridge and attend journal clubs (embedded in doctoral training centre: www.nanodtc.cam.ac.uk) 
• Experienced users will train the student in using both the SEM and the robotic arm, meeting with the candidate daily at the start of the project and adjusting as the project progresses

Essential Knowledge, Skills, and Attributes 

• Experience with Python, NumPy and Pandas in particular. Custom Python-based software for the SEM has been developed, and the robot control system would be an add-on to this interface. 
• Familiarity with coding closed-loop control algorithms, data preprocessing, and hardware-software integration (e.g. from an Arduino project). 
• Eagerness to work in an interdisciplinary environment. The project will focus on software development, but the broader context involves electron microscopy, nanomaterial growth, and rapid prototyping. 

Skills and attributes that would be advantageous

• It would be particularly valuable if the candidate were familiar with Git, image recognition techniques, forward and inverse kinematics, Linux, or serial communications. 

Timing

Application closing date: 17th April 2026. 

The project start date is flexible but ideally would begin in early July. 

Supporting Information 

https://doi.org/10.1038/s42254-025-00875-9">A recent example of 2D material growth using capillaries in the SEM. 

Broader context: https://doi.org/10.1038/s42254-025-00875-9">problems in 2D-material growth. 

Link to Hofmann group: hofmann-group.eng.cam.ac.uk

Link to NanoDTC: www.nanodtc.cam.ac.uk 

Application Details

Please email Prof Stephan Hofmann, sh315@cam.ac.uk, with a copy of your CV along with a short statement in your email explaining why you are interested in this particular project.

Deadline for applications: 17^th April 2026.

 

Understanding future critical mineral demand for energy, aerospace, AI and defence

Primary Supervisor Details 

Dr André Cabrera Serrenho

Department of Engineering

ag806@cam.ac.uk">mailto:ag806@cam.ac.uk">ag806@cam.ac.uk

Co-Supervisors / Industrial Collaborators

Dr Sam Stephenson

Department of Engineering

sds70@cam.ac.uk">mailto:sds70@cam.ac.uk">sds70@cam.ac.uk

Dr Philip Mitchell

Department of Engineering

pmm73@cam.ac.uk">mailto:pmm73@cam.ac.uk">pmm73@cam.ac.uk

Project Description

Demand for critical minerals such as lithium, cobalt, copper and rare earth elements is expected to grow rapidly over the coming decades. Much of this growth is driven by clean energy technologies like electric vehicles, batteries and solar panels. While these sectors are relatively well studiedhttps://universityofcambridgecloud-my.sharepoint.com/personal/jgg44_cam_..." name="_ftnref1" title="">[1]https://universityofcambridgecloud-my.sharepoint.com/personal/jgg44_cam_..." name="_ftnref2" title="">[2], far less is known about how demand for critical minerals could grow in other fast growing technologies, including artificial intelligence and semiconductors, defence and aerospace systems, and industrial equipment.

This project builds on ongoing research developed as part of the Climate Compatible Growth research programme. Our research team has already compiled a database that links different technologies (such as batteries and renewable energy systems) to the critical minerals they contain. The goal of this project is to expand that database to new sectors, helping researchers better understand how future technology trends could shape global mineral demand.

The Climate Compatible Growth research programme is a flagship programme funded by the UK Foreign, Commonwealth and Development Office that seeks to support economic growth in the global south through climate action. The successful applicant will have the opportunity to work with a team of international researchers driven to make a tangible and meaningful contribution to the twin problems of climate change and economic development.

The successful student will work closely with the research team and will be responsible for:

• Identifying sectors and technologies that are likely to have critical minerals embodied in them.
• Conduct an analysis of literature, reports and other databases to understand how much critical minerals are embodied in each technology. The students will then form part of the decision making team to prioritise which technologies and minerals to focus on in future research.
• Using their newfound knowledge to help the research team develop new scenarios for technology development. This may include collecting data to help inform the scenarios for example GDP, government defence spending, ai capital investment, which could all be used to model future changes in demand.
• Support the research team to implement new data and scenarios into existing models.
• Produce a report setting out which sectors are likely to contribute to future critical mineral demand growth, the important minerals in each sector and how each sector might grow or evolve in the future.

This internship offers a hands-on introduction to applied research at the intersection of technology, sustainability and policy. The student will make a meaningful contribution to research with real world relevance and will gain experience working with researchers studying the entire critical mineral supply chain. It is well suited to undergraduate students with an interest in sustainability, engineering, economics, data analysis or related fields.

Essential Knowledge, Skills, and Attributes 

• Desk based research skills, including the ability to review academic, business and other technical documents and extract relevant information
• Knowledge of critical minerals and their use in manufacturing
• Ability to work independently

Skills and attributes that would be advantageous

• Experience coding in python
• Detailed understanding on one or more of the key sectors for critical minerals (ai and semi-conductors, defence, aerospace)
• Understanding of economic data and how it can be correlated with other activities

Timing

8 weeks over the summer, with exact dates flexible and to be agreed with the student.

Supporting Information 

Please see links to the research group (www.ccml.org.uk/">https://www.ccml.org.uk/">CCML) and wider research project (https://climatecompatiblegrowth.com/">climate compatible growth).

Application Details

Please email Dr André Cabrera Serrenho, ag806@cam.ac.uk, with a copy of your CV along with a short statement in your email explaining why you are interested in this particular project.

Deadline for applications: 20^th April 2026

Supercomputing Finite Element Models with GPUs 

Prof Garth Wells, gnw20@cam.ac.uk 

Dr Chris Richardson, Earth Sciences, cnr12@cam.ac.uk 

Dr Joseph Dean, Engineering, jpd62@cam.ac.uk 

Project Description

Finite Element Analysis underpins much of the simulation of engineering models, from jet engines to nuclear fusion components. As models become more complex, the size of the simulation increases, and it is no longer possible to solve on a laptop or even a workstation. For the most challenging models, we need to use High Performance Computing (HPC) resources, such as the University of Cambridge CSD3 machine. The latest hardware on HPC systems is trending towards GPUs. GPUs are more energy efficient, and can perform a vast number of computations in parallel, however programming them is challenging. 

In this project, we will investigate some algorithms that can be used for high performance computation on GPUs, e.g. for finite element kernels, or for physical processes such as radiative heat transfer between surfaces. The student will have access to HPC computing resources, and powerful modern computational GPU devices to run their code.  

The project will support the development of high-demand, transferable skills, including GPU programming, computing for mathematical problems, software engineering, and using remote, high-performance computing systems.  

Strong programming skills in Python. Basic understanding of linear algebra, systems of equations. Know how to use revision control systems (git). Good communication skills, and ability to read and understand technical documentation. 

C++ programming, CAD. 

Timing

8 consecutive weeks during the months June-September.

See the FEniCS Project, https://fenicsproject.org and https://github.com/FEniCS

 

Integrated Photonics and Metasurfaces for Quantum Computing with Trapped Ions and Neutral Atoms

Primary Supervisor Details 

Dr. Amit Agrawal

Department of Engineering (Div B)

aka59@cam.ac.uk

Project Description

Quantum computing and quantum sensing with trapped ions and neutral atoms represent some of the most promising routes toward practical quantum technology. A central challenge in scaling these platforms is the delivery and control of laser light with high precision, stability, and complexity. These tasks are currently performed using large, expensive, and fragile free-space optical setups. This project involves designing, fabricating, and testing nanophotonic chips and metasurfaces capable of replacing bulky and sensitive optical tables with a miniaturised and robust chip-based system. 

The student will join an active research group working at the intersection of integrated photonics and quantum computing architectures. Over eight weeks, they will contribute to the development of scalable nanophotonic interfaces used for trapping and addressing neutral atoms and trapped ions used in next-generation quantum computers, atomic clocks and quantum sensors.

Design and Simulation: The project will include with computational design of photonic chip components and metasurface structures. The student will use simulation tools including Rigorous Coupled-Wave Analysis (RCWA) for metasurface design and Finite-Difference Time-Domain (FDTD) methods for photonic waveguide and grating structures. They will learn how to set up simulation geometries, define material parameters, and interpret electromagnetic field outputs to build a library of metasurface elements. Inverse design techniques, where target optical functionalities are specified and algorithms search for optimal structures, will also be introduced, giving the student exposure to cutting-edge computational photonics methods for device optimisation. Data analysis and visualisation of simulation outputs will form an important component of this phase, building transferable skills in scientific computing.

The student will also be involved in the optical test laboratory to characterise fabricated photonic components. This will involve working with visible and near-infrared laser systems, optical fibres, free-space alignment, and photodetection equipment relevant to atomic physics wavelengths (e.g. 780 nm, 852 nm, 729 nm). The student will measure transmission efficiency and beam profiles, comparing experimental results directly against simulation predictions to benchmark and characterize the fabricated components. This hands-on experience with precision optical instrumentation is directly applicable to careers in both academic research and the photonics industry but will also give general experience on how to work in a lab with many different and sophisticated instruments.

There might also be potential scope to do some work in the cleanroom for material deposition and lithography although that is heavily dependent on access, timeline and the applicant’s own goals.

Skills and Career Development: By the end of the project, the student will have developed practical skills in electromagnetic simulation, optical lab work and scientific data analysis. This combination is highly valued across quantum technologies and photonics and in many other engineering disciplines. The project is well-suited to students with backgrounds in physics, electrical engineering, or materials science, and provides an excellent foundation for postgraduate study or industry roles in the rapidly growing quantum technology sector.

Essential Knowledge, Skills, and Attributes 

• Python – data analysis, OOP, any simulation experience (preferred, not necessary)
• Basic knowledge of optics/electromagnetics 
• Organised approach to lab work – especially important when working with lasers

Skills and attributes that would be advantageous

• Simulation work of any kind, Electromagnetics preferred
• Data analysis workflows using python
• Some optics experience (optical fiber use, lasers etc.)

Timing

8 weeks, dates flexible from the end of term June 2026.

Application Details 

Please email Dr. Amit Agrawal, aka59@cam.ac.uk,, with a copy of your CV along with a short statement in your email explaining why you are interested in this particular project.

Deadline for applications: April 17th, 2026

 

Siemens-Energy Summer Research Opportunity Project 2026

Primary Supervisor Details

Andrew Wheeler

Whittle Laboratory

aw329@cam.ac.uk

Co-Supervisors / Industrial Collaborators

Roger Wells
Siemens Energy

Project Description

The project is an exciting opportunity to work at the forefront of the energy transition, investigating the long-term shift from a system dominated by fossil fuels to one based on low-carbon sources. The project will make use of systems modelling approaches to investigate important technologies likely to affect the energy transition with a focus on climate impact, cost and security of supply.

The project will be based at the Whittle Laboratory, University of Cambridge, and last between 8-10 weeks (typically between July and October). The candidate will be expected to work closely with engineers at Siemens Energy during the project. Siemens Energy operates across the whole energy landscape; from conventional to renewable power, from grid technology to storage to electrifying complex industrial processes. With over 102,000 employees in four divisions, present in over 90 countries worldwide Siemens Energy technology provides ~ 1/6^th of the world’s electricity generation.

Essential Knowledge, Skills, and Attributes

The candidate will have an outstanding academic track record. The candidate will have knowledge of thermofluids and thermodynamics. Some experience of software and coding development is desirable. Relevant industrial experience is also desirable. The candidate will usually be a current undergraduate studying engineering or a relevant degree. Students at post-graduate level may also apply.

Timing

Applications open from now. Closing date 24^th April 2026.

The project start date can be flexible but will typically be in July. The project will last for up to 10 weeks.

Continuation Opportunities

It is possible for the project to lead to a 4^th year project.

Supporting Information

Further information about the Whittle Laboratory and Siemens Energy can be found at these links:

https://whittle.eng.cam.ac.uk/">whittle.eng.cam.ac.uk

www.siemens-energy.com/">http://www.siemens-energy.com/">siemens-energy.com

Application Details

Please email Prof. Andrew Wheeler, aw329@cam.ac.uk, with a copy of your CV, along with a short statement in your email explaining why you are interested in this particular project.

Deadline for applications: 24^th April 2026