Postdoctoral Research Associate Position in Neuromorphic Accelerators

Location: Greater London

## Postdoctoral Research Associate Position in Neuromorphic Accelerators Apply locations:
London, UKtime type:
Full time posted on:
Posted 2 Days Agotime left to apply:
End Date:
August 15, 2026 (19 days left to apply) job requisition :
R141061
** About the Opportunity
**** Postdoctoral Research Associate Position in Neuromorphic Accelerators
**** Position Overview
**** Discipline
* * Engineering
* * Faculty
* * Institute for Intelligent Networked Systems (INSI)
** Location
* * London, Devon House (St Katherines Dock)
** Term
* * 1.5 years, fixed term, full-time
** Salary Range**£42,701- £48,035 per annum - dependent on experience
** Reports to
** Professor Bipin Rajendran
** Benefits
* * The university supports staff maintaining a good work-life balance, offering: (
*** i***) flexible working and parental leave opportunities; (
*** ii***) an employee assistance programme which provides free, confidential advice on both home and work concerns, as well as optional private medical insurance; (
*** iii***) season ticket loans; and (
*** iv***) being part of the cycle-to-work scheme.
** Start
* * September 2026
** The Role
** Northeastern University London invites applications for a Post-doctoral Research Associate (PDRA) in Neuromorphic Accelerators to work with Professor Bipin Rajendran as part of the Neuro Ware Neuromorphic Computing Hardware Semiconductor IKC.The successful candidate will work on the design, implementation and validation of hardware accelerators for state-of-the-art AI models based on neuromorphic computing and In-Memory Computing (IMC) principles for applications in enterprise, IoT and Networked Systems.

Core duties include:
* Contribute to the design, implementation, and evaluation of neuromorphic and in-memory hardware accelerators for training and inference of state-of-the-art AI models.
* Develop algorithms, architectures, and hardware–software co-design methodologies for energy-efficient AI computation on neuromorphic and in-memory computing (IMC) platforms.
* Collaborate with cross-disciplinary teams spanning hardware design, AI/ML algorithms, information theory, and systems software to enable end-to-end system integration and validation.
* Publish high-quality research outcomes in leading journals and conferences in neuromorphic computing, AI hardware, and computer architecture.
* Support the supervision and mentoring of PhD and MSc students within the research group.
* Engage with industrial and academic collaborators to support joint research activities, technology transfer, and translational impact.
* Contribute to project management activities, including technical reporting, proposal development, and dissemination of research outcomes.

We particularly encourage applications from those belonging to groups underrepresented in UK higher education.
** About the Institute
** The Intelligent Networked Systems Institute (INSI) at NU London is a newly established transatlantic research hub focused on next-generation connectivity and intelligent computing and learning systems. The INSI London Hub acts as a bridge between the US and Europe, advancing interdisciplinary collaboration across continents. INSI is dedicated to addressing pressing challenges in telecommunications, artificial intelligence, quantum information, and information processing—all with the objective of creating a more efficient, connected, and secure world.
** Person Specification Criteria
** To undertake this role, the following should apply – should you not have the experience below, please do highlight where transferable skills would assist with you undertaking the role.
*** Qualifications
* *** PhD in Computer Science, Electronics or Computer Engineering or related quantitative field.
*** Key Criteria
**** Strong publication record in neuromorphic hardware design demonstrated through publications in leading conferences and/or journals, with expertise extending beyond materials and device optimisation toward system- and architecture-level innovation.
* Experience in the simulation, implementation, and validation of large-scale machine learning models, including probabilistic, neuromorphic, Transformer, and/or state-space models.
* Experience with…