Postdoctoral Research Associate in Efficient Fine-Tuning Algorithms Neuromorphic Language Models

About the Opportunity

Position Overview

Discipline:
Engineering

Faculty:
Computing Mathematics Engineering and Natural Sciences (CoMENS)

Location:

London Devon House (St Katherines Dock)

Term: 12 months

Salary Range: 42701 per annum

Reports to:

Professor Osvaldo Simeone

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.

February 2026

Northeastern University London invites applications for a Post-Doctoral Research Associate in Efficient Fine-Tuning Algorithms for Neuromorphic Language Models to work with Professor Osvaldo Simeone and Professor Bipin Rajendran. This position is funded under an ARIA grant aimed at democratizing AI via brain-inspired energy-efficient training and adaptation.

The successful candidate will join a research group working on this project to develop theoretically principled energy-efficient and reliability-aware frameworks for next‑generation AI with special emphasis on neuromorphic and spiking architectures for language modeling. Key application domains include edge intelligence, low-power autonomous systems and communication networks.

• Efficient fine-tuning of spiking / neuromorphic language models: sparse updates, weight quantization, modulatory adaptation and task-dependent reconfiguration.
• Neuromorphic hardware‑software co‑design including simulation and prototyping of energy‑aware spiking systems for scalable inference and adaptation.
• Publishing results in leading journals and conferences in machine learning, information theory and quantum information.
• Presenting research findings at project meetings, workshops and international symposia.
• Supporting the supervision and mentoring of PhD students and research assistants within the group.
• Contributing to the preparation of project deliverables, reports and future funding proposals.

We particularly encourage applications from those belonging to groups underrepresented in UK higher education.

The Institute for the Wireless Internet of Things (WIoT) at Northeastern University London focuses on advancing next‑generation wireless systems and intelligent connectivity. Building on WIoT's global research leadership the London campus brings together expertise in AI neuromorphic computing and wireless communications to explore transformative technologies for 6G and beyond. The institute fosters close collaboration with industry and academia, providing an interdisciplinary environment for innovation, hardware prototyping and impactful real‑world research.

The Faculty of Computing Mathematics Engineering and Natural Sciences (CoMENS) is undergoing significant growth at Northeastern University London. It is home to four interdisciplinary undergraduate dual‑degree programmes in the areas of Data Science, Data Science and Politics, Computer Science and Business, and Computer Science and Philosophy; four postgraduate programmes in the areas of AI Ethics, Data Science, Computer Science and Technology Leadership;

and five degree apprenticeship programmes in the areas of AI, Data Science, Digital and Technology Solutions and Biosciences.

The faculty plays a major part in Northeastern University’s first‑year student mobility programme offering undergraduate courses in Computer and Data Science, Mathematics, Engineering, Physics, Biology, Chemistry and Healthcare, striving to inspire and equip entering students to be outstanding scientists.

To undertake this role the following should apply; if you do not have the experience below please highlight where transferable skills would assist you in undertaking the role.

• PhD or equivalent professional experience in Electrical Engineering, Computer Science or a related field.

• Demonstrated experience with mathematical modelling, algorithm design or theoretical analysis of learning systems.
• Proficienc…