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Only in the last three decades, cement and plastic production has grown 2.5-fold, glass 2 and steel 1.5-fold (Cullen J.M., Drewniok M.P. et al. 2020). In 2022, the global building sector accounted for 34% of energy demand and 37% of total energy related CO2 emissions, reaching nearly 10 Gt CO2 (Hamilton, Kennard et al. 2024). More than a quarter were related to production of cement, steel, aluminium, bricks and glass (embodied carbon). It is predicted that global building stock should almost double by 2050 to meet population growth needs (GABC and IEA 2017 ). In the UK context, the built environment already accounts for nearly 30% of the UK’s total territorial GHG emissions (Green, Jonca et al. 2021), with the main materials used in construction accounting for up to 6% (Drewniok, Azevedo et al. 2023). As demand for construction is expected to increase (residential, commercial, non-emitting carbon infrastructure), we expect the use of materials to increase.

Emissions reduction techniques during manufacture (e.g. using alternative fuels, increase resource efficiency in production) can only slightly reduce rather than entirely eliminate the emissions related to construction materials. Moving to the most materially and carbon efficient technology options for buildings can bring further savings (Drewniok, Azevedo et al. 2023) with the largest savings occurring in structural efficiency (Dunant, Drewniok et al. 2021). Nevertheless, this will not allow to reach net-zero carbon by 2050. It is therefore crucial to minimise the overall flow of materials in the UK construction – dematerialisation (Drewniok, Azevedo et al. 2023).

In the presentation we will try to analyse the extent to which dematerialisation should be implemented in the UK construction industry to minimise the emissions from UK construction.

• Speaker: Michal Drewniok, University of Leeds, UK
• Friday 14 February 2025, 15:00-16:00
• Venue: CivEng Seminar Room (1-33) (Civil Engineering Building).
• Series: Engineering Department Structures Research Seminars; organiser: Shehara Perera.

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The Department of Engineering is the largest department in the University of Cambridge with approximately 160 academics, 350 research staff, 800 graduate students, 1200 undergraduates and 800 professional staff. The annual expenditure is approximately GBP100m of which GBP50m is research grant expenditure

Based at the Department's site in Central Cambridge, we are looking for an experienced finance professional to deputise for, and work under the guidance of, our Head of Finance to set financial strategy and operations across the entire Department. You will be covering key aspects of the Head of Finance role, as well as undertaking Department project work and engaging with change management initiatives as directed by the Head of Finance. As financial systems and regulations evolve in the University, you will, working alongside the Head of Finance, play a leading and influential role at School and University levels in providing insights, advice and direction on policy and processes.

You will also be required to deputise for the Head of Finance in the management of the operation of the Department's Finance Office and Purchasing & Stores, with overall management responsibility for the associated staff in these teams, as well as the Research Grants team, as required. In this regard, you will be leading and supporting finance professionals across the Department, raising standards, developing and delivering new training, and opening career paths

The successful candidate will be educated to degree level/level 7 vocational qualification, or an equivalent level of practical experience, and hold an accounting qualification, such as CIMA, ACA, ACCA or CIPA, and be able to evidence up-to-date professional development and knowledge. As well as being a qualified accountant, we require the successful candidate to have significant post-qualified operational experience gained in complex matrix organisations and be experienced in management accounting, business modelling, presenting complex data, optimisation and data-driven decision making. Given that you will be managing a large number of staff, the successful candidate is required to have demonstrable experience of managing and developing large professional teams. Additionally, you will possess excellent interpersonal and communication skills with the ability to tailor communications effectively to different audiences and will be a creative problem solver who has strong influencing and analytical skills. Higher education sector experience would be desirable.

Fixed-term: The funds for this post are available for 5 years in the first instance.

Click the 'Apply' button below to register an account with our recruitment system (if you have not already) and apply online.

Please ensure that you upload your Curriculum Vitae (CV) and a covering letter in the Upload section of the online application.

The closing date for applications is Tuesday 25 February 2025. If you have any questions about this vacancy or the application process, please contact the HR Office at the Department of Engineering (hr-office@eng.cam.ac.uk, +44 (0)1223 332615).

Please quote reference NM44766 on your application and in any correspondence about this vacancy.

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.

The University has a responsibility to ensure that all employees are eligible to live and work in the UK.

A position exists, for a Research Associate/Senior Research Associate in the Department of Engineering, to work on Gallium Nitride quantum photonic devices.

The post holder will be located in West Cambridge, Cambridgeshire, UK.

The Research Associate/Senior Research Associate will join the Integrated Quantum Photonics group, based in the Nanoscience Centre at the University of Cambridge, a research group that works on simulation, nanofabrication and optical characterisation of nano- and quantum photonic devices, integrating quantum light emitters. They will work on a research project focused on the investigation of the emission properties of quantum dots and defect centres in Gallium Nitride, in particular when coupled to plasmonic devices, like metallic nano-rings [see Applied Physics Letters 111, 021109 (2017), Applied Physics Letters 112, 221102 (2018), Applied Physics Letters 120, 081103 (2022), Advanced Quantum Technologies 2300149 (2023)]. The goal of the project is to realise a monolithic, scalable source of quantum light for quantum communication and imaging.

Education & qualifications: Applicants should have a PhD in Physics, Materials Science, Engineering, or related areas.

For the Senior Research Associate role, the role holder would have at least three years of postdoctoral research experience at the level of Research Associate, or equivalent experience.

Specialist knowledge & skills: Knowledge of quantum photonics, quantum optics, nano-photonics, nanofabrication, Finite-Difference Time-Domain simulations, quantum dot spectroscopy. Experience in spectroscopy and quantum optics is highly desirable. Experience in simulations and nanofabrication are desirable but not essential.

For the Senior Research Associate role, the role holder would possess sufficient breadth/depth of specialist knowledge in the discipline and of research methods and techniques to develop research objectives, projects and proposals. They will continually update knowledge in the specialist area and engage in continuing professional development.

The initial appointment will be for 12 months in the first instance. Candidates will be considered for the appropriate role based on their experience matching the criteria.

Fixed-term: The funds for this post are available for 12 months in the first instance.

Click the 'Apply' button below to register an account with our recruitment system (if you have not already) and apply online.

Please ensure that you upload your Curriculum Vitae (CV), including a research publication list, and a covering letter in the Upload section of the online application. If you upload any additional documents which have not been requested, we will not be able to consider these as part of your application. Please submit your application by midnight on the closing date.

If you have any questions about this vacancy, please contact Dr Luca Sapienza (LS2052@cam.ac.uk) for queries of a technical nature related to the role or for queries on the application process, please contact: Sue Murkett email: sm330@cam.ac.uk (Tel + 44 1223 760304).

Please quote reference NM44965 on your application and in any correspondence about this vacancy.

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.

The University has a responsibility to ensure that all employees are eligible to live and work in the UK.

The Cambridge Service Alliance (CSA) is a research centre situated within the Institute for Manufacturing (IfM) in West Cambridge. It serves as a collaborative platform for knowledge exchange and partnership with industry, bringing together leading firms and academic experts to address the challenges of digital service transformation through innovative research and educational initiatives. The CSA aims to engage with industry to shape the research agenda, support education, disseminate knowledge arising from research, and prepare the industry for digital service challenges in the next 3 to 5 years.

We are looking for an enthusiastic and experienced individual to play a pivotal role in promoting and maintaining relationships with existing industry partners, defining and steering the research agenda in collaboration with these partners, and identifying and securing new members and partnerships with the industry. You will work closely with the Deputy Director of the CSA and will be the main contact for their Industry Partners

You will be working internationally to establish new founding members and expand the CSA's presence in various regions. The successful candidate will facilitate knowledge exchange, support the generation of research themes, and ensure that the CSA's activities align with both academic excellence and industry needs. A key aspect of the role includes developing and submitting research grant proposals to local and international funding bodies to sustain and expand the CSA's research initiatives.

The successful candidate will be educated to degree level in a related subject or have an equivalent level of experience, and a PhD, Masters or equivalent research experience is desirable. You will possess excellent interpersonal and communication skills with the ability to work collaboratively within a multidisciplinary team and academic environment. You will be a strategic thinker who is able to align partnership activities with the CSA's mission and objectives. Proven experience in industry partnership management, business development, or a related role within a research or academic environment is required, and previous experience in organising and facilitating workshops, meetings, and collaborative events is also essential.

Fixed-term: The funds for this post are available for 3 years in the first instance.

Click the 'Apply' button below to register an account with our recruitment system (if you have not already) and apply online.

The closing date for applications is Tuesday 4 March 2025. If you have any questions about this vacancy or the application process, please contact the HR Office at the Department of Engineering (hr-office@eng.cam.ac.uk, +44 (0)1223 332615).

Please quote reference NM44560 on your application and in any correspondence about this vacancy.

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.

The University has a responsibility to ensure that all employees are eligible to live and work in the UK.

Inferring model parameters from observational data of a physical system is the setup for many inverse problems. Solving these kinds of problems can give key insight into the state of a system for quantities that are not directly observable, such as material properties. In this talk, we discuss a population-based perspective on solving inverse problems where the data available comes from a collection of physical systems and we are interested in characterising the (indirectly observable) properties of these systems at a distributional level. We call this: calibrating priors from indirect data. Furthermore, we show how this can be accomplished while concurrently learning ML-based surrogates which capture the behaviour of the physical systems of interest.

This talk has been canceled/deleted

• Speaker: Arnaud Vadeboncoeur, University of Cambridge
• Friday 07 February 2025, 15:00-16:00
• Venue: CivEng Seminar Room (1-33) (Civil Engineering Building).
• Series: Engineering Department Structures Research Seminars; organiser: Shehara Perera.

Add to your calendar or Include in your list

Inferring model parameters from observational data of a physical system is the setup for many inverse problems. Solving these kinds of problems can give key insight into the state of a system for quantities that are not directly observable, such as material properties. In this talk, we discuss a population-based perspective on solving inverse problems where the data available comes from a collection of physical systems and we are interested in characterising the (indirectly observable) properties of these systems at a distributional level. We call this: calibrating priors from indirect data. Furthermore, we show how this can be accomplished while concurrently learning ML-based surrogates which capture the behaviour of the physical systems of interest.

• Speaker: Arnaud Vadeboncoeur, University of Cambridge
• Friday 07 February 2025, 15:00-16:00
• Venue: CivEng Seminar Room (1-33) (Civil Engineering Building).
• Series: Engineering Department Structures Research Seminars; organiser: Shehara Perera.

Add to your calendar or Include in your list

Inferring model parameters from observational data of a physical system is the setup for many inverse problems. Solving these kinds of problems can give key insight into the state of a system for quantities that are not directly observable, such as material properties. In this talk, we discuss a population-based perspective on solving inverse problems where the data available comes from a collection of physical systems and we are interested in characterising the (indirectly observable) properties of these systems at a distributional level. We call this: calibrating priors from indirect data. Furthermore, we show how this can be accomplished while concurrently learning ML-based surrogates which capture the behaviour of the physical systems of interest.

• Speaker: Arnaud Vadeboncoeur, University of Cambridge
• Friday 07 February 2025, 15:00-16:00
• Venue: CivEng Seminar Room (1-33) (Civil Engineering Building).
• Series: Engineering Department Structures Research Seminars; organiser: Shehara Perera.

Add to your calendar or Include in your list

Inferring model parameters from observational data of a physical system is the setup for many inverse problems. Solving these kinds of problems can give key insight into the state of a system for quantities that are not directly observable, such as material properties. In this talk, we discuss a population-based perspective on solving inverse problems where the data available comes from a collection of physical systems and we are interested in characterising the (indirectly observable) properties of these systems at a distributional level. We call this: calibrating priors from indirect data. Furthermore, we show how this can be accomplished while concurrently learning ML-based surrogates which capture the behaviour of the physical systems of interest.

• Speaker: Arnaud Vadeboncoeur, University of Cambridge
• Friday 07 February 2025, 15:00-16:00
• Venue: CivEng Seminar Room (1-33) (Civil Engineering Building).
• Series: Engineering Department Structures Research Seminars; organiser: Shehara Perera.

Add to your calendar or Include in your list

Inferring model parameters from observational data of a physical system is the setup for many inverse problems. Solving these kinds of problems can give key insight into the state of a system for quantities that are not directly observable, such as material properties. In this talk, we discuss a population-based perspective on solving inverse problems where the data available comes from a collection of physical systems and we are interested in characterising the (indirectly observable) properties of these systems at a distributional level. We call this: calibrating priors from indirect data. Furthermore, we show how this can be accomplished while concurrently learning ML-based surrogates which capture the behaviour of the physical systems of interest.

• Speaker: Arnaud Vadeboncoeur, University of Cambridge
• Friday 07 February 2025, 15:00-16:00
• Venue: CivEng Seminar Room (1-33) (Civil Engineering Building).
• Series: Engineering Department Structures Research Seminars; organiser: Shehara Perera.

Add to your calendar or Include in your list

Inferring model parameters from observational data of a physical system is the setup for many inverse problems. Solving these kinds of problems can give key insight into the state of a system for quantities that are not directly observable, such as material properties. In this talk, we discuss a population-based perspective on solving inverse problems where the data available comes from a collection of physical systems and we are interested in characterising the (indirectly observable) properties of these systems at a distributional level. We call this: calibrating priors from indirect data. Furthermore, we show how this can be accomplished while concurrently learning ML-based surrogates which capture the behaviour of the physical systems of interest.

• Speaker: Arnaud Vadeboncoeur, University of Cambridge
• Friday 07 February 2025, 15:00-16:00
• Venue: CivEng Seminar Room (1-33) (Civil Engineering Building).
• Series: Engineering Department Structures Research Seminars; organiser: Shehara Perera.

Add to your calendar or Include in your list

Inferring model parameters from observational data of a physical system is the setup for many inverse problems. Solving these kinds of problems can give key insight into the state of a system for quantities that are not directly observable, such as material properties. In this talk, we discuss a population-based perspective on solving inverse problems where the data available comes from a collection of physical systems and we are interested in characterising the (indirectly observable) properties of these systems at a distributional level. We call this: calibrating priors from indirect data. Furthermore, we show how this can be accomplished while concurrently learning ML-based surrogates which capture the behaviour of the physical systems of interest.

• Speaker: Arnaud Vadeboncoeur, University of Cambridge
• Friday 07 February 2025, 15:00-16:00
• Venue: CivEng Seminar Room (1-33) (Civil Engineering Building).
• Series: Engineering Department Structures Research Seminars; organiser: Shehara Perera.

Add to your calendar or Include in your list

Inferring model parameters from observational data of a physical system is the setup for many inverse problems. Solving these kinds of problems can give key insight into the state of a system for quantities that are not directly observable, such as material properties. In this talk, we discuss a population-based perspective on solving inverse problems where the data available comes from a collection of physical systems and we are interested in characterising the (indirectly observable) properties of these systems at a distributional level. We call this: calibrating priors from indirect data. Furthermore, we show how this can be accomplished while concurrently learning ML-based surrogates which capture the behaviour of the physical systems of interest.

• Speaker: Arnaud Vadeboncoeur, University of Cambridge
• Friday 07 February 2025, 15:00-16:00
• Venue: CivEng Seminar Room (1-33) (Civil Engineering Building).
• Series: Engineering Department Structures Research Seminars; organiser: Shehara Perera.

Add to your calendar or Include in your list

Inferring model parameters from observational data of a physical system is the setup for many inverse problems. Solving these kinds of problems can give key insight into the state of a system for quantities that are not directly observable, such as material properties. In this talk, we discuss a population-based perspective on solving inverse problems where the data available comes from a collection of physical systems and we are interested in characterising the (indirectly observable) properties of these systems at a distributional level. We call this: calibrating priors from indirect data. Furthermore, we show how this can be accomplished while concurrently learning ML-based surrogates which capture the behaviour of the physical systems of interest.

• Speaker: Arnaud Vadeboncoeur, University of Cambridge
• Friday 07 February 2025, 15:00-16:00
• Venue: CivEng Seminar Room (1-33) (Civil Engineering Building).
• Series: Engineering Department Structures Research Seminars; organiser: Shehara Perera.

Add to your calendar or Include in your list

A PhD studentship is available to work on Logistics automation. The student appointed will work with the Distributed Information and Automation Laboratory (DIAL) at the Institute for Manufacturing (IfM).

One of the key areas of focus for the project is to systematically analyse the impact of automation for small and medium scale enterprises. This includes, but is not limited to, the automation solutions developed through the ongoing Shoestring Logistics project at IfM. Examples of such automation are warehouse automation, autonomous delivery vehicles coordination, human-robots coordination, etc. A second focus is the development of novel algorithms and systems to support logistics automation systems such as these.

Applicants should have (or expect to obtain by the start date) at least a good 2.1 degree (and preferably a Masters degree) in Engineering or Physical Sciences.

This studentship will cover home University fees and a maintenance allowance of at least £19,000 per year.

Applicants should have (or expect to obtain by the start date) at least a good 2.1 degree in an Engineering or related subject.

Applications should be submitted via the University of Cambridge Applicant Portal www.graduate.study.cam.ac.uk/courses/directory/egegpdpeg, with Professor Duncan McFarlane identified as the potential supervisor.

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.

This opportunity is part of the part of the DREAM + PLUS - Co-fund programme. Further information can be found at; https://www.dreamplusplan.eu

Project 1: The digital integration of product lifecycle information and R-process models to provide real-time insights for circular economy business decisions. The realisation of circular economy-based business models is currently limited by a lack of digital information, tools and insights to support operational decisions. While industry has recently started to acquire large volumes of product lifecycle data, tools and platforms are still required to generate information and insights to effectively support decisions. Furthermore, the implications (e.g. yields, emissions, costs) of R-processes (e.g. recycle, remanufacture, refurbish), which are central to the circular economy, are not accessible to decision makers in a definite and timely fashion. This topic will develop an architecture and platform to integrate product lifecycle information and digital R-process models for on-demand insights.

Project 2: Development of an intelligent agent-based platform to support the dynamic exploration of opportunities for circular economy across supply chains. Several recent global events have exposed the volatility, vulnerability and importance of supply chains. The supply chains that will drive the circular economy is expected to exhibit even more volatility, since these supply chains will likely be disturbed by frequent changes in supply and demand, as well as the introduction and enforcement of international regulations. This topic will develop a digital marketplace platform, based on AI agents, that can explore and negotiate opportunities within and across supply chains on behalf of business actors. Agents in this ecosystem will be able to share information, execute strategies (likely employing machine learning or large language models) and interface with human decision makers.

Project 3: Development of an intelligent product platform to enable product-driven decisions and operations for a circular economy. This topic will focus on the development of a digital platform that maintains the digital representations of physical products, which allow these products to execute autonomous functions related to their contribution to a circular economy. For instance, the digital representations of products may alert decisions makers of optimal timing/conditions for the execution of R-processes, based on acquired product lifecycle data and the deployment of prediction and optimization models. This product-driven mechanism will serve as a key enabler for the prioritization of circularity as a means towards sustainability within existing business models.

With, or expected to gain a high 2:1, preferably a 1st class honours degree or master's in Engineering or STEM topics

Applications should be initially submitted via the DREAM+PLAN portal https://www.dreamplusplan.eu/offered-phd-positions. More instructions will be circulated during the selection process https://www.dreamplusplan.eu/application-process

Please note that any offer of funding will be conditional on securing a place as a PhD student. Candidates will need to apply separately for admission through the University's Graduate Admissions application portal; this will be done after applying and being shortlisted for this funding opportunity.

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.

This opportunity is part of the part of the DREAM + PLUS - Co-fund programme. Further information can be found at; https://www.dreamplusplan.eu

This candidate will work in one of the following outlined areas:

Project 1: Integrating digital technologies in the boardroom: options and potential of emerging digital tools to augment socio-cognitive decision-making processes This research investigates the integration of emerging digital technologies such as Artificial Intelligence (AI), Virtual Reality (VR), Simulation, or Digital Twins into corporate boardrooms to enhance socio-cognitive decision-making and support sustainable strategic transitions. Climate uncertainty and ecosystem degradation are forcing firms to reconsider their strategic approaches and adopt innovative, regenerative practices. However, socio-cognitive challenges, such as entrenched mindsets and resistance to change, could limit the effectiveness of existing tools like roadmapping and scenario planning. Emerging technologies offer transformative potential to augment current tools and frameworks. AI can analyze complex datasets to identify trends, predict outcomes, and provide actionable insights, allowing managers to make data-driven decisions. VR and immersive simulation can facilitate experiential learning, enabling leaders to visualize the impacts of strategic choices on environmental and social systems. Digital Twins can create real-time, virtual replicas of organizational processes and external systems, offering a sandbox for testing strategies before implementation. However, as the digital technologies are emerging, we do not have yet guidelines about how to integrate them and our DM-ET Group has started to work in this direction. This new project aims to build an understanding of the principles for integrating these technologies with established decision-support tools. Collaborating with IfM Engage, this research will involve participating and supporting companies in their journey towards the development of sustainable transitions experiments with organizations to explore the options and evaluate the potential of one of these tools. This project seeks to enable firms to make transformative, future-proof decisions and establish leadership in sustainability transitions but exploring the obstacles faced by industry today, developing guidance on how digital technologies could be integrated in analogical processes and potentially provide prototypes of their integration for testing.

Project 2: Bridging across elements under tension in companies' sustainable transitions: balancing short- and long-term visions, profit- and value-driven logics This research project aims to address the critical challenge of aligning short-term corporate actions with long-term sustainability objectives. As businesses face increasing pressure to adapt to climate uncertainties and resource constraints, the ability to balance immediate operational needs with transformative, forward-looking strategies becomes essential. However, many organizations struggle to achieve this balance, as short-term decision-making often takes precedence due to financial, market, and stakeholder pressures. In collaboration with IfM Engage, the project will explore methodologies and tools that enable firms to bridge the gap between decision tensions such as short-term imperatives and long-term sustainable transitions, economic or social value-driven logics. It will investigate the role of integrated decision-making frameworks such as scenario planning, roadmapping, or other approaches in aligning organizational visions across different time horizons, and for transitioning from incremental to transformational innovation.

This project might also/alternatively involve an analysis of the models and systems (i.e. the outcome of the decision-making processes) that balance desirability, profitability, and sustainability in innovation or/and of the process that helps bridging these socio-cognitive and organizational barriers via using combinations of approaches. In collaboration with IfM Engage, building on past work at our DM-ET Group, the project will ground research in the real world in real-world corporate environments, ensuring practical relevance and impact. Outcomes of this research might deliver a framework for fostering coherence between short-term actions and long-term strategies, tools for visualizing the implications of decisions across time horizons, and actionable guidelines for embedding sustainability into the corporate agenda. By bridging these perspectives, this project seeks to empower organizations to achieve resilience, competitiveness, and sustainability in a rapidly evolving global landscape.

Project 3: The role of collaboration in sustainable decisions: how is open innovation implemented to support sustainable transitions? This research project examines how collaborative frameworks, particularly through Open Innovation (OI), can be implemented to drive sustainable transitions within organizations. Amidst growing climatic uncertainties, businesses face immense pressure to adopt regenerative and transformative strategies. Collaboration is essential in addressing these challenges, as no single entity possesses all the resources or expertise required to navigate the complexities of sustainability. Open Innovation, which promotes the sharing of ideas, knowledge, and resources across organizational boundaries, offers significant potential to enable this transition. How are companies using Open innovation mechanisms to push the building of ecosystems? With ecosystems we mean the "evolving set of actors, activities, and artifacts, and the institutions and relations, including complementary and substitute relations" (Grandstrand and Holgersson (2020, p3), particularly those (sustainable ecosystems) which consider improving the environment as their main goal (Pham and Vu, 2022). What are the socio-cognitive and organizational barriers which hinder the adoption of collaborative strategies or limit their effectiveness and how are they alleviated? Building on past research in OI at our DM-ET Group, this research will explore the dynamics of collaboration in sustainable decision-making, focusing on how OI principles can overcome barriers and lead to meaningful outcomes. In collaboration with IfM Engage, this research will investigate how firms co-create innovative solutions with stakeholders, such as suppliers, customers, and even competitors, to address sustainability challenges. Case studies of successful OI implementations will be analyzed to identify best practices and lessons learned which can be translated in decision-making support tools and practices.

With, or expected to gain a high 2:1, preferably a 1st class honours degree or master's in Management, Sociology, Economics, Engineering, Neuroscience, Psychology or related areas.

Applications should be initially submitted via the DREAM+PLAN portal https://www.dreamplusplan.eu/offered-phd-positions. More instructions will be circulated during the selection process https://www.dreamplusplan.eu/application-process

Please note that any offer of funding will be conditional on securing a place as a PhD student. Candidates will need to apply separately for admission through the University's Graduate Admissions application portal; this will be done after applying and being shortlisted for this funding opportunity.

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.

The Aviation Impact Accelerator (AIA) at the University of Cambridge seeks to recruit a Technical Analyst based at the Whittle Laboratory in West Cambridge. The research carried out by the AIA is critically dependent upon robust modelling and analysis to support its mission of transforming aviation toward a net-zero future. This role has been created to align with the design and delivery of innovative tools and services that underpin cutting-edge research in sustainable aviation.

To assist the AIA in achieving its objectives, you will develop and operate models that assess the economic and environmental outcomes of aviation systems. You will also contribute to external reports and grant proposals under the supervision of the line manager, while engaging with stakeholders across academia, industry, and policy domains to ensure impactful delivery of research outputs.

You will have an undergraduate degree in STEM (engineering, mathematics, physics, environmental sciences, or a related field), or equivalent practical experience. You will possess strong software skills preferably in Python, alongside proficiency in modelling and data analysis. Excellent verbal and written communication skills are essential, as is the ability to work both independently and as part of a team. Previous experience in techno-economic analysis, lifecycle modelling, or related areas will be advantageous.

This is an exciting opportunity to contribute to meaningful research that influences global aviation policies while developing your expertise in a collaborative and innovative environment.

Fixed-term: The funds for this post are available until 31 July 2025 in the first instance.

Applications are welcome from internal candidates who would like to apply for the role on the basis of a secondment from their current role in the University.

Click the 'Apply' button below to register an account with our recruitment system (if you have not already) and apply online.

Please ensure that you upload your Curriculum Vitae (CV) and a covering letter in the Upload section of the online application.

The closing date for applications is Wednesday 12 February 2025. If you have any questions about this vacancy or the application process, please contact the HR Office at the Department of Engineering (hr-office@eng.cam.ac.uk, +44 (0)1223 332615).

Please quote reference NM41778 on your application and in any correspondence about this vacancy.

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.

The University has a responsibility to ensure that all employees are eligible to live and work in the UK.

We are looking to appoint a skilled process technician who will be based in the Electrical Engineering Division which is situated at one of the Department of Engineering's site in West Cambridge, to support the development of a polymer composite-based force sensor.

You will primarily be working with a small team of doctoral/postdoctoral researchers in the clean facilities and characterisation laboratories in the Electrical Engineering Division, including in the Cambridge Graphene Centre Small Research Facility (SRF) to support the. research and training activities of the project.

The successful applicant will have a qualification equitable to HND/HNCC, level 4/5, vocational qualifications or an equivalent level of practical experience. You should also possess excellent communication and IT skills with a working knowledge of relevant regulations and good practice for working in a microfabrication clean facility. Practical experience in a clean environment for scientific work is essential for this role and knowledge in the area of mechanical testing of materials would also be desirable.

Occasional travel within the UK to work with other companies may be required as occasional weekend working to help set up and participate in public dissemination activities.

Benefits of working at Cambridge include:

• Competitive rates of pay with automatic service-related pay progression and annual cost of living increases;

• Generous annual leave allowance;

• Flexible working opportunities;

• Generous maternity, adoption and shared parental leave entitlement and other family friendly schemes (e.g. workplace nurseries and salary exchange schemes for childcare);

• An auto-enrolment pension scheme, with a generous employer contribution;

• Travel benefits and retail discounts at over 2,000 local and national stores;

• Schemes to support with relocation / the provision of accommodation;

Fixed-term: The funds for this post are available for 3 years in the first instance.

Click the 'Apply' button below to register an account with our recruitment system (if you have not already) and apply online.

The closing date for applications is Tuesday 11 February 2025. If you have any questions about this vacancy or the application process, please contact the HR Office on 01223 332615 or by email on hr-office@eng.cam.ac.uk

Please quote reference NM44877 on your application and in any correspondence about this vacancy.

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.

The University has a responsibility to ensure that all employees are eligible to live and work in the UK.

A position exists, for a Research Assistant/Associate in the Department of Engineering, to work within Quantum Networking activity within the Centre for Photonic Systems.

The post holder will be located in CUED's Electrical Division Building in West Cambridge.

The key responsibilities and duties are to develop Quantum Entanglement networks in Cambridge, with a particular emphasis on efficient post-processing to improve operational performance in field trials. This will involve design, construction and assessment of quantum communication sub-systems and post processing software modules. There will also be optimisation of post processing on CV-QKD and Quantum Alarm systems within Cambridge.

The qualifications required to perform the role are to have obtained or be close to obtaining a PhD in Electronic Engineering, Physics or a related discipline or have equivalent industrial experience in photonics, preferably in quantum communications. Preference will be given to candidates with demonstrated experimental and software aptitude within Quantum Communications of research and an ability to work within a team.

Appointment at Research Associate level is dependent on having a PhD. Those who have submitted but not yet received their PhD will be appointed at Research Assistant level, which will be amended to Research Associate once the PhD has been awarded.

The Salary Range is Research Assistant: £32,296 - £34,866 and Research Associate: £36,924 - £45,163

Fixed-term: The funds for this post are available until 31 March 2026 in the first instance.

Click the 'Apply' button below to register an account with our recruitment system (if you have not already) and apply online.

Please ensure that you upload your Curriculum Vitae (CV), a research publications list and a covering letter in the Upload section of the online application. If you upload any additional documents which have not been requested, we will not be able to consider these as part of your application. Please submit your application by midnight on the closing date.

If you have any questions about this vacancy, please contact Professor Richard Penty (rvp11@cam.ac.uk) for queries of a technical nature related to the role or Mrs Kam Lundin, Electrical Engineering Division, Department of Engineering, 9 J. J. Thomson Avenue, Cambridge, CB3 0FA, (Tel +44 01223 748341, email cps-sec@eng.cam.ac.uk) for questions on the application process.

Please quote reference NM44871 on your application and in any correspondence about this vacancy.

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.

The University has a responsibility to ensure that all employees are eligible to live and work in the UK.

Exceptional individuals sort to develop a new type of advanced hydrogen jet engine. We are seeking exceptional individuals with a range of skills (hydrogen, heat exchangers, turbomachinery, mechanical design, amongst others) to join our Rapid Technology Development Team in developing a new generation of advanced hydrogen jet engines for long-haul flights. The project is moving at incredible speed and requires people with a flexible mindset, who are prepared to work across the boundary of applied science and its emerging application in a transformative technology. The challenge is immense, but successful candidates can expect to play a central part in the transformation of flight.

It has recently been realised that when the fuel which powers flight is changed from kerosene to cryogenic hydrogen that it is possible to design a new type of jet engine which has the potential to reduce the energy required for flight by around 20% below the current most efficient jet engines. Inspired by Sir Frank Whittle's pioneering team that created the first jet engine, our mission is to assemble an elite group of innovators to design the first concept of this new type of engine.

This ambitious project - funded by Lord Sainsbury, Peter Bennett, and Rolls-Royce - has been specifically designed to bypass the constraints of traditional government and industry funding. The core team structure and funding has been specifically designed for speed ¿ with the potential to 'flip' the entire engine design in days rather than years. Leveraging the Whittle Lab's cutting-edge rapid technology development capabilities, the team has the ability to prototype sub-technologies in days or weeks, not months or years.

The post holder will be located in Whittle Laboratory in West Cambridge, Cambridgeshire, UK.

The appointment at Research Associate level is dependent on having a PhD (or equivalent experience). Those who have submitted but not yet received their PhD will be appointed at Research Assistant level, which will be amended to Research Associate once the PhD has been awarded.

Research Assistant: £32,296-£34,866 Research Associate: £36,924-£45,163

Fixed-term: The funds for this post are available for 24 months in the first instance.

Click the 'Apply' button below to register an account with our recruitment system (if you have not already) and apply online.

Please ensure that you upload your Curriculum Vitae (CV), a covering letter and research publication list in the Upload section of the online application. If you upload any additional documents which have not been requested, we will not be able to consider these as part of your application.

If you have any questions about this vacancy or the application process, please contact Professor Robert Miller (rjm76@cam.ac.uk) for queries of a technical nature related to the role, and Juliet Teather (jet63@cam.ac.uk) for queries related to the application process.

Please quote reference NM44841 on your application and in any correspondence about this vacancy.

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.

The University has a responsibility to ensure that all employees are eligible to live and work in the UK.