Feed aggregator

Structural engineering spans a wide spectrum of practice, and the work at Cake Industries is very much at the “applied” end, where theory becomes real. Based on several key projects this talk will endeavour to look at how working right at the intersections of design, engineering and making can lead to developments in what is possible.

• Speaker: David Knight, Cake Industries
• Friday 02 February 2024, 15:00-16:00
• Venue: CivEng Seminar Room (1-33) (Civil Engineering Building).
• Series: Engineering Department Structures Research Seminars; organiser: Callum White.

Add to your calendar or Include in your list

We are excited to announce an EPSRC-funded PhD studentship opportunity in the field of computational neuroscience, in the Department of Engineering, University of Cambridge.

Aims. This PhD project uses an information engineering approach to unravel the dynamic neural mechanisms orchestrating the maintenance of body homeostasis and endogenous pain regulation. Endogenous pain regulation refers to the intrinsic ability of the central nervous system to modulate the intensity and perception of pain. Unlike exogenous interventions such as medications, endogenous regulation involves the body's internal mechanisms for controlling and mitigating harm. This intricate process encompasses a network of neural circuits and neurotransmitter systems that dynamically interact to either enhance or suppress the transmission of bodily signals.

Methods. The project involves developing and using computational methods to analyse neural dynamics in the brain and spinal cord involved in homeostasis and endogenous regulation of pain. These involve the use of large-scale simulations of neural activity using neural network models. The incorporation of control theory principles enhances the study by providing a systematic framework for analyzing the regulatory processes within the neural network. Control theory, commonly applied in engineering contexts, allows for the modeling and understanding of feedback loops and adaptive responses within complex systems¿attributes highly relevant to the dynamic nature of endogenous pain regulation. There will also be the opportunity to work with high-resolution neuroimaging data, if desired.

Expected outcomes. This multidimensional investigation not only seeks to advance our understanding of endogenous pain regulation but also holds promise for the development of targeted interventions for chronic pain conditions. By elucidating the neural substrates and dynamic patterns governing pain modulation, this research may contribute to the refinement of therapeutic strategies and the advancement of personalized pain management approaches.

Candidate profile. With, or expected to gain a high 2:1, preferably a 1st class honours degree in Engineering, Neuroscience, Computer Science, Physics/Mathematics or related discipline.

Environment. The Computational and Biological Learning group uses engineering approaches to understand the brain and to develop artificial learning systems. Research includes Bayesian learning, computational neuroscience, statistical machine learning and sensorimotor control. The work on machine learning includes both theory and applications to vision, information retrieval and bioinformatics. The work on human learning includes both computational modelling and experimental approaches using robotic and virtual reality interfaces. More info: https://www.cbl-cambridge.org/

https://www.noxlab.org/

This PhD opportunity is fully-funded (fees and maintenance) for eligible UK students. EU and international students may be considered for a small number of awards at the UK rate. Full eligibility criteria can be found via the following link: https://www.postgraduate.study.cam.ac.uk/finance/fees/what-my-fee-status

To apply for this studentship, please send your two page CV and statement of research interests to Dr Mancini flavia.mancini@eng.cam.ac.uk to arrive no later than 17/03/2024. Applications may close early if the position is filled before this date.

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 can be done before or after applying for this funding opportunity. The applicant portal can be accessed via: www.graduate.study.cam.ac.uk/courses/directory/egegpdpeg. The final deadline for PhD applications is 16 May 2024, although it is advisable to apply earlier than this.

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

The mission of the Cambridge Graphene Centre (CGC) is to investigate the science and technology of graphene and related materials. The CGC also hosts the EPSRC Centre for Doctoral Training (CDT) in Graphene Technology, and the Layered Materials Research Foundry (LMRF), as well as the Quantum and Advanced Materials technologies for a Sustainable Society (QAMSS) Strategic Research Initiative (SRI). The CDT works closely with industry to ensure that successful students are properly equipped to follow careers in both industry and academia. The LMRF provides wafer-scale integration of Layered Materials (LMs) on up to 8" substrates, and silicon photonics multi-project wafers, in collaboration with CORNERSTONE at the University of Southampton. The CGC SRF caters for well over 100 users from academia and industry, both within and outside Cambridge, and the UK.

The CGC is a very busy and dynamic environment and we are seeking an experienced administrator to join us in West Cambridge to coordinate the administrative tasks of the Centre, ensuring that the Director of the CGC can work effectively. Reporting to the Director regularly, you will be responsible for assessing and responding to issues, providing a wide range of administrative services, and referring more complex queries as appropriate. You will be actively engaged with a large variety of stakeholders, both internal and external to the University. A high volume of enquires are received every day from a diverse range of individuals, and the Centre's reputation relies on a friendly and efficient interface with all who make contact. This is crucial in securing funds, and to maintain and establish good relationships with industries, universities, funding bodies, media, etc.

You will be educated to degree level or have an equivalent level of practical experience with previous experience in an administrative role within a busy office environment. You will possess excellent communication and interpersonal skills with the ability to interact with a wide range of people. Excellent organisational skills are also an essential requirement for this role as well as significant experience of Microsoft Office.

Fixed-term: The funds for this post are available for 1 years 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.

The closing date for applications is Monday 12 February 2024. 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. Informal enquiries can be made to Professor Andrea C. Ferrari (acf26@eng.cam.ac.uk).

Please quote reference NM40281 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 seeking an EDI, Training and Wellbeing Coordinator, who will be based at the Department's central Cambridge site, to support and deliver on wellbeing, equality, diversity, and inclusion priorities in the Department. The institutional culture among the staff and students is a major priority for the Head of Department of Engineering and the HR Team to enhance the sense of belonging and mutual respect.

Reporting directly to the Departmental Head of HR, and as a member of the wider Departmental HR team working across both the Department's HR Offices, you will support the department in striving forward for initiatives such as Athena Swan and the Race Equality Charter. You will use your HR experience to lead on these and other EDI initiatives, which will include coordinating the Department's Athena Swan application. Additionally, you will be responsible for organising training and other activities to support all categories of staff within the Department.

By supporting the design and delivering an EDI and training strategy, you will assertively navigate multiple stakeholders demands and will ensure that the priorities of the respective Departmental Divisions are considered as part of the broader departmental EDI and training and development initiatives, ensuring that they align to the Department's overarching purpose, values and Departmental priorities with the internal culture and employee experience.

The successful candidate will be educated to degree level or equivalent qualification or be able to evidence quantifiable experience and an Associate CIPD qualification would be desirable. A working knowledge of UK employment law and HR best practice coupled with knowledge of University HR Policies and procedures is essential to ensure compliance with relevant legislation. To be effective in the role, you will have the authority and judgement to build and maintain professional relationships with a diverse range of people at all levels both internal and external to the University. Significant experience of working in an HR/EDI/training and development role in a busy institution, ideally in an academic environment, is an essential requirement for this role.

If you think you are the right candidate for this role, we would love to hear from you.

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 Monday 12 February 2024. 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 NM40192 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.

Within the construction industry, there are established legacy processes which predate the digital age.

These processes facilitate the delivery of projects and collate documentation for future reference. However, often, data is collected without structure or digitisation which hinders value creation from knowledge management and does not allow for big data analytics. Physical forms, unstructured excel and visual data (such as drawings and photos) form a large proportion of the information collected and used on construction projects.

At the same time, some of those digital tools intended to replace these manual methods are not designed with the users and realities of physical sites in mind making their adoption very hard. From feedback received through industry engagement, a significant barrier to the adoption of data-driven technology is that the interfaces are complicated, lengthy, and non-intuitive. Digital literacy among construction worksite employees is low, therefore, digital data capture processes are seen as a barrier and hinderance to normal operations instead of a value creator for the project.

This EPSRC iCASE-funded PhD project will develop modern systems of data capture to ensure data quality for the efficient management (and future improvement) of construction projects. This will include consideration of modern data science including ML (machine learning), AI (Augmented Intelligence), LLM (large language models) and design frameworks for human-data interfaces (human centric design, system thinking, UI/UX). The outputs of this project will increase the uptake of digital tools and data-driven processes in industry to facilitate more efficient decisions, ensure safety and reduce wastage.

-What are the critical data points captured on project sites and shared between project and organisational teams for knowledge exchange and continuous learning?

-What are the decision points on a complex delivery project?

-What are automatable tedious tasks that create cognitive fatigue to workers?

-How do we leverage modern digital design frameworks (e.g. UX/UI, software, human centred design) to improve human-data interfaces?

-What are the design principles and unique considerations for digital tools meant for construction workers?

-How much data collection can be automated or streamlined using visual and natural language interface/capture?

-How do we maximise quality of data collected? How does this look with off-site construction?

-How do we expediate the feedback loop between collection and visualisation/realisation of benefits?

-How do the data collection methods allow for flexibility and evolution of the construction project data landscape?

-What skills are needed by construction staff?

-What are the potential ethical considerations of automation?

EPSRC iCASE studentships are fully-funded (fees and maintenance) for eligible UK students. Full eligibility criteria can be found via the following link; https://www.postgraduate.study.cam.ac.uk/finance/fees/what-my-fee-status

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 Ioannis Brilakis identified as the potential supervisor.

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

A position exists, for a Research Assistant/Associate in the Department of Engineering, to work on biomechanical modelling of the spinal injury Degenerative Cervical Myelopathy [DCM].

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

The key responsibilities and duties will be:

• Developing finite element (FE) methods to predict stress on the spinal cord for DCM patients
• Undertaking finite element (FE) modelling incorporating patient data
• Comparing FE results with markers extracted from clinical data
• Compiling and analysing quantitative and qualitative data, preparing reports to summarise main findings and conclusions.

The project is in close collaboration with clinical colleagues at Addenbrooke's Hospital in Cambridge.

The skills, qualifications and experience required to perform the role are that the applicant must have obtained, or be close to obtaining, a PhD in engineering or a similar technical subject, with expertise in finite element modelling of complex structures and in bioengineering/biomechanics.

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.

Salary Ranges: Research Assistant: £29,605 - £33,966 Research Associate: £36,024 - £44,263

Fixed-term: The funds for this post are available until 31 July 2025 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. 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 of a technical nature related to the role, please contact Professor Michael Sutcliffe at mpfs@eng.cam.ac.uk), and for those related to the application processs, contact Claire Whitaker, Divisional Administrator, at div-c@eng.cam.ac.uk.

Please quote reference NM40246 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 have funding for a number of 4-year PhDs, in collaboration with industry, on net zero infrastructure and built environment under the four following themes:

-Current and disruptive technologies

-Circularity and whole life approach

-AI, digitalisation and data

-Risk-based systems thinking and connectivity

Studentships are fully-funded (fees and maintenance) for eligible UK students.

For technical enquires please e-mail Professor Abir Al-Tabbaa (aa22@cam.ac.uk). For general enquiries, please email Ella Harding-Rolls (emh64@cam.ac.uk)

Applicants should have (or expect to obtain by the start date) a good 2.1 degree, preferably at masters level, in a relevant engineering or science discipline.

Applications should be submitted for the PhD in Engineering via the University of Cambridge Applicant Portal https://www.postgraduate.study.cam.ac.uk/courses/directory/egegpdpeg, with Professor Abir Al-Tabbaa identified as the potential supervisor.

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

Understanding the structure and function of networks is central to our understanding of complex systems. This project will work on the development of both models and inference techniques for the principled statistical analysis of large-scale complex networks.

Data may be drawn from a variety of domains, such as epidemiology, ecology, neuroscience, economics, and the social sciences. Likewise, methods will be drawn from across statistics, machine learning, physics, and applied mathematics. The goal will be to create algorithms to analyse disparate systems, along with an understanding of their theoretical limits.

Applicants should have (or expect to obtain by the start date) at least a high 2:1 honours degree in engineering, physics, computer science, mathematics, or a related subject.

Students with an interest in networks and complex systems and with training in any quantitative subject are encouraged to apply.

EPSRC DTP studentships are fully-funded (fees and maintenance) for eligible UK students. EU and international students may be considered for a small number of awards at the UK rate. Full eligibility criteria can be found via the following link; https://www.postgraduate.study.cam.ac.uk/finance/fees/what-my-fee-status

Applications should be submitted via the University of Cambridge Applicant Portal https://www.postgraduate.study.cam.ac.uk/courses/directory/egegpdpeg/finance, with Dr George Cantwell identified as the potential supervisor.

Interviews will be held in two rounds, with the first at the end of February.

Applications will close early if the position is filled before the advertised date.

For further details, contact Dr George Cantwell at gtc31@cam.ac.uk

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

A position exists, for a Research Assistant/Associate in the Department of Engineering, to work with the Research Hub for Decarbonised Adaptable and Resilient Transport Infrastructures (DARe). The researcher will play a key role in developing techniques and algorithms to model the impact of climate change and related policies/scenarios on the performance and behaviour of infrastructure assets and systems, and approaches to optimise their whole-life cost and performance.

The post holder will be located in West Cambridge, Cambridgeshire, UK. The post holder will be primarily based in the Asset Management Group at the Institute for Manufacturing, but will work closely with the Cambridge Centre for Smart Infrastructure and Construction.

The key responsibilities and duties are: developing whole-life performance models for critical infrastructure assets and systems and analysing the value of sensing technologies in improving asset operation and maintenance. In particular, the researcher will interact and engage with our project partners (industrial and academic) and other key stakeholders in defining the problem(s), identifying possible paths for solution development and developing decision-support tools. The researcher will be expected to produce high-quality publications for presentation at academic and industrial conferences in addition to the top journals in the field.

Applicants must have (or be close to obtaining) a PhD in Engineering or relevant discipline. The researcher's expertise includes infrastructure asset management and whole-life cost/performance analysis, reliability engineering, network modelling, and optimisation. The applicants will be expected to demonstrate ability to engage with industrial partners, develop and deliver high-quality presentations, publish high-quality research papers in top-tier journals and conferences. Applicants will have experience in modelling and optimisation of complex infrastructure systems and networks and will be expected to demonstrate experience in working with industrial partners and real industrial problems.

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.

Salary Ranges: Research Assistant: £29,605 - £33,966 Research Associate: £36,024 - £44,263

Fixed-term: The funds for this post are available for 18 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) 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 or the application process, please contact Bet Fowler by email: dial-admin@eng.cam.ac.uk

Please quote reference NM40231 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.

Abstract not available

• Speaker: Neil Thomas, Atelier One
• Friday 26 January 2024, 15:00-16:00
• Venue: CivEng Seminar Room (1-33) (Civil Engineering Building).
• Series: Engineering Department Structures Research Seminars; organiser: Jamie Clarkson.

Add to your calendar or Include in your list

Climate change and human demands are threatening water supplies worldwide. To respond to water scarcity, countries around the world are increasingly investing in desalination. Over the last decade, the costs of desalination have dropped, making this technology affordable for a broader range of contexts and uses. Global installed desalination capacity reached 61.8 million m3/day in 2020 (equivalent to 4.5 percent of global domestic water use). In the next two years, scenarios suggest that an additional capacity of 26.1 million m3/day (equivalent to 42 percent of current capacity) will be contracted out. Moreover, interest in green hydrogen is expected to further boost the demand for desalination.

To help advance desalination in a sustainable and strategic manner, we need to quantify its energy and material requirements at the global scale - this is the aim of this research project. The project will provide the first global assessment of the joint energy and material requirements of desalination throughout its entire life cycle. It will do so by tracing materials and energy flows for desalination at the global level, identify any bottlenecks, and suggest policy and technology options for managing its energy and material requirements.

We are looking for a PhD student to lead this project. This is an exciting opportunity to shape research on an emerging water technology.

You will be a great fit if: - You have a First-Class or High Upper-Second Class master's degree in engineering, earth sciences, economics, statistics, or computer science. - You enjoy working with data and have experience translating ideas into efficient computer code. - You are interested in exploring the potential - and the limits - of new technologies in addressing climate change impacts. - You want to understand more about the link between emerging energy technologies, such as green hydrogen, nanomaterials, such as graphene, and the world's water supplies. - You ask yourself how the world will meet its energy, material and water needs in a sustainable way. - In 5 years, you see yourself working for the public or private sector on solutions to advance climate mitigation and adaptation. - You enjoy working with colleagues from different disciplinary backgrounds and nationalities. - You have previous research or professional experience on related topics [note: desirable but not essential]

You will join the University of Cambridge's Centre for Sustainable Development, and work alongside world-leading experts on water, energy, and material systems. The Centre is part of the Department of Engineering, which is consistently ranked among the top five engineering departments in the world. In Cambridge, you will also be exposed to the University's growing initiatives to develop solutions to address urgent environmental challenges, such as Cambridge Zero and the Cambridge Conservation Initiative.

At the end of your PhD, you will be equipped with the skills and expertise to work on global water policy and the sustainable roll-out of desalination - a growing technology. You will gain expertise in desalination, water management, and life cycle assessment methodologies. You will be able to work on water and energy policy for the public or private sectors or embark upon an academic career in engineering and technology.

Applicants should have (or expect to obtain by the start date) at least a good 2.1 degree in an Engineering, Economics or Computer Science.

This PhD opportunity is fully-funded (fees and maintenance) for eligible UK students. EU and international students may be considered for a small number of awards at the UK rate. Full eligibility criteria can be found via the following link: https://www.postgraduate.study.cam.ac.uk/finance/fees/what-my-fee-status

To apply for this studentship, please send a two-page CV and a one-page cover letter explaining why you are a good fit to Dr Edoardo Borgomeo (eb973@cam.ac.uk) to arrive no later than 5pm on March 18, 2024. Your CV should indicate the names of two references to be contacted in case you are shortlisted for the position.

You are encouraged to apply early, as applications may close early if the position is filled before March 18, 2024. Interviews (virtual) will take place shortly after this date.

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 can be done before or after applying for this funding opportunity. The applicant portal can be accessed via: www.graduate.study.cam.ac.uk/courses/directory/egegpdpeg. The final deadline for PhD applications is 16 May 2024, although it is advisable to apply earlier than this.

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

This project seeks to develop a process model for the reuse of wastewater streams for hydrogen production and direct integration into industrial feedstocks, and to use techno-economic analysis to evaluate the feasibility of doing so, using real facility data as case studies.

Currently, nearly all hydrogen is produced from fossil fuels. Industries which use hydrogen as a feedstock could reduce their carbon footprint by using renewable energy to electrolyse water.

This work will identify where this type of process integration would be feasible, considering cost, hydrogen need, and wastewater makeup. It will identify industries which might benefit from this type of process intensification, and then will extend to gather specific data about the wastewater constituency from various factories or industries. It will develop case studies identifying the specific makeup of the wastewater, what the current disposal method is, what the current source of hydrogen is, specific constituents which may be a problem for electrolyzers, and specific constituents which are of strong environmental concern.

This project will then develop an appropriate process train using unit operations and process modelling for the purification and electrolysis for hydrogen production of wastewater for specific industries. This will be supported by a techno-economic analysis to understand the economic implications of implementing such a system. This will be extended by optimizing the process train and operating point to maximize financial viability or minimize the environmental load. It will provide specific recommendations for economically viable methods for implementing and integrating these methods.

Outcomes from this work include a set of case studies of industries which benefit most from reusing wastewater to produce hydrogen; a set of process models to describe the energy, additive, material and equipment needs of the process; and a techno-economic analysis model to optimize process designs for financial cost or environmental burden. The model will be applied to the case studies and analysed for potential improvements to the process.

This project encourages the circular resource economy and identifies strategies for decarbonizing feedstocks from critical industries through process integration of wastes and feedstocks. It will provide specific recommendations and pathways to reduce carbon dioxide emissions, and the environmental load from wastewater releases.

Applicants should have (or expect to obtain by the start date) at least a good 2.1 degree in engineering, or similar. Background knowledge of electrochemistry, water chemistry, or computational methods is beneficial.

EPSRC DTP studentships are fully-funded (fees and maintenance) for eligible UK students. EU and international students may be considered for a small number of awards at the UK rate. Full eligibility criteria can be found via the following link; https://www.postgraduate.study.cam.ac.uk/finance/fees/what-my-fee-status

To apply for this studentship, please send your two-page CV to Dr. Nathanial Cooper at nc646@cam.ac.uk no later than 16 February 2024. Applications may close early if the position is filled before this date.

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 can be done before or after applying for this funding opportunity. The applicant portal can be accessed via: www.graduate.study.cam.ac.uk/courses/directory/egegpdpeg. The final deadline for PhD applications is 16 May 2024, although it is advisable to apply earlier than this.

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

A position exists, for a Research Assistant/Associate in the Department of Engineering, to work on a collaborative project between the labs of Guillaume Hennequin and Máté Lengyel at the Computational and Biological Learning Lab, Department of Engineering, University of Cambridge (https://cbl.eng.cam.ac.uk), and Daniel Wolpert at the Zuckerman Mind Brain Behavior Institute, Columbia University (https://wolpertlab.neuroscience.columbia.edu/people/daniel-wolpert).

The post holder will be located in Central Cambridge, Cambridgeshire, UK, with opportunities to visit Columbia University in New York, USA.

The project studies neural network mechanisms underlying the context-dependent (continual) learning of motor repertoires and ties together several threads of research recently developed in our labs, based on the following key publications (note that several are collaborative between our groups):

• JB Heald, M Lengyel, DM Wolpert (2021) Contextual inference underlies the learning of sensorimotor repertoires. Nature, https://rdcu.be/cBYhM
• TC Kao, MS Sadabadi, G Hennequin (2021). Optimal anticipatory control as a theory of motor preparation: a thalamo-cortical circuit model. Neuron, 109:1567-1581.
• TC Kao, KT Jensen, GM van de Ven, A Bernacchia, G Hennequin (2021) Natural continual learning: success is a journey, not (just) a destination. NeurIPS, https://tinyurl.com/2jfyss8c
• Echeveste R, Aitchison L, Hennequin G, Lengyel M (2020) Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference. Nature Neuroscience 23: 1138-1149.

The successful candidate(s) will have:

• a strong quantitative background
• demonstrable interest in theoretical neuroscience
• obtained (or be close to the completion of) a PhD or equivalent in computational neuroscience, physics, mathematics, computer science, machine learning or a related field.

Interviews will be done via Zoom.

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.

Salary Ranges: Research Assistant: £29,605 - £33,966 Research Associate: £36,024 - £44,263

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) 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, Profs Lengyel or Hennequin may be contacted; for queries of a technical nature related to the role or the application process, please contact the CBL group administrator by email cbl-enquiries@eng.cam.ac.uk.

Please quote reference NM40145 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 seeking a post-doctoral Research Associate ("post-doc") to join the Machine Learning Group (MLG) (http://mlg.eng.cam.ac.uk) in the Department of Engineering, University of Cambridge, UK. This position will contribute to the research programme "Deep Learning and AI Alignment".

The programme's goal is to increase our understanding of how to align AI systems -- especially those based on Deep Learning -- with human preferences/values/intentions. The post-doc will work with Assistant Professor David Krueger on topics in AI Alignment, Deep Learning and/or Reinforcement Learning. They will have the opportunity to work with members of the MLG including Professor Richard Turner and Dr. Adrian Weller, and other professors at Cambridge, such as Assistant Professor Samuel Albanie, and Associate Professor Ferenc Huszár. They will also have the opportunity to assist in supervising PhD students, Master's students, and research assistants.

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

Key responsibilities include working on AI alignment. Current topics of particular interest are: Mechanistic interpretability and science of deep learning, scaling and foundation models such as large language models, foundations of model evals, and red teaming existing alignment proposals and identifying/understanding novel/underexplored failure modes (e.g., limitations of fine-tuning based protocols for alignment).

Additional responsibilities include developing research objectives and proposals; presentations and publications; assisting with teaching; liaising and networking with colleagues and students; planning and organising research resources and workshops.

Successful applicants will have or be near to completing a PhD in computer science, information engineering, statistics, or another relevant area, with extensive research experience and a strong publication record. Excellent mathematical and programming skills are essential.

Interviews are expected to happen in January 2023 at the Department of Engineering of Cambridge University. Applicants are encouraged to guarantee that referees can submit their letters before such date. The interviews will be done via online video conference.

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.

Research Assistant: £29,605 - £33,966 Research Associate: £36,024 - £44,263

Fixed-term: The funds for this post are available until 31 December 2025 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 list of your publications, 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 you may contact David Krueger, dsk30@cam.ac.uk; for the application process, please contact: CBL administrator, email: cbl-enquiries@eng.cam.ac.uk.

Please quote reference NM40140 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.