Research Associate* in NeuroAI, Neuromorphic Systems, Hardware-Software Co-Design

About the role:

This exciting and broadly scoped Research Associate* position, hosted at EEE at Imperial College London, is funded by the Advanced Research and Innovation Agency (ARIA) Nature Computes Better Opportunity Seed. This work is aiming to redefine the way computers process information by exploiting principles found ubiquitously in nature and advancing alternative computing paradigms. The position is deliberately broad in scope to attract candidates with broad and diverse backgrounds and interests.

The successful candidate will be integral to continue delivering on the project: "Brain-inspired spatially embedded neuromorphic networks with unprecedented memory", while also contributing to broader research in algorithms and heterogeneous systems. Possible projects include, but not limited to:

1. Investigating the scaling behaviour of time-based quantised spiking neural networks.
2. Applying parameter-free attention techniques to novel challenging neuromorphic tasks.
3. Building new neuromorphic datasets which require “cocktail party effect” attention mechanisms to solve successfully.

The scope of the project is wide, encompassing novel algorithmic approaches and hybrid systems. We actively encourage applications from candidates with varied academic and technical backgrounds. Core competency in computational methods is essential, and backgrounds in neuromorphic systems, alternative computing architectures, or heterogeneous systems are highly desirable.

The successful candidate will work closely with the PI Dr Danyal Akarca (Imperial) and team Prof Daniel Goodman (Imperial) and Dr Jascha Achterberg (Oxford). Regular meetings will be conducted with the ARIA team.

What you would be doing:

• Your research will be dynamic, with the scope to pivot and explore new opportunities as they arise, including novel algorithmic approaches and unconventional computing paradigms, reflecting the innovative nature of the project.
• You will be continuing work already established within the lab, training neural networks to neuromorphic benchmark tasks, identifying the extent to which temporal parameters improve performance and efficiency.
• You will start quickly and effectively, leveraging your experience in machine learning, alternative algorithms, and/or heterogeneous computing to contribute from the onset.
• You will liaise with external collaborators and stakeholders, including hardware vendors and research communities focused on emerging computing technologies, to ensure the project's success and alignment with broader research goals.
• You will prepare and present research findings at academic conferences and in peer-reviewed journals, covering topics from neuromorphic computing to broader alternative computing paradigms.
• You will mentor other junior researchers and PhD students involved in the project, fostering a collaborative and educational environment that encourages exploration of unconventional computational approaches.
• You will maintain detailed documentation of research activities and outcomes to ensure reproducibility and transparency across different computing platforms and algorithmic approaches.
• Your role may include contributing to the development of grant proposals and reports to secure additional funding and support for ongoing research initiatives.
• You will actively participate in departmental seminars, workshops, and other professional development opportunities to enhance your skills and knowledge in cutting-edge computational approaches.

What we are looking for:

• Educational background: PhD in Computer Science, Neuroscience, Electrical Engineering, Physics, Mathematics, or a related discipline with focus on computational systems, algorithms, or hardware.
  • *Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant (see salary scales).
• Advanced computing expertise: Significant experience in computational methods, which may include machine learning, alternative algorithms, or novel computational approaches. This could involve training neural networks, constructing new architectures and learning objectives, or developing unconventional algorithmic solutions.
• Computational systems knowledge: Strong background in at least one of: computational modelling, algorithm development, system optimisation, or emerging computational technologies, with demonstrated ability to work across different computing domains.
• Research experience: Proven track record of conducting high-quality research, as evidenced by publications, conference presentations, or significant project contributions appropriate to career stage.
• Programming skills: Proficiency in programming languages commonly used in computational research, such as Python, C++, or specialised languages for computing platforms. Experience with computational libraries (e.g., PyTorch, JAX) is essential, with experience or willingness to learn platform-specific tools.
• Communication: Strong verbal and written communication skills, with the ability to present complex information clearly and concisely across different technical domains.
• Collaborative skills: Excellent teamwork and communication skills, with experience collaborating with interdisciplinary teams and external partners from diverse computational backgrounds.
• Problem-solving ability: Strong analytical and problem-solving skills, with the ability to adapt to new challenges and pivot research focus as needed across different computational paradigms.
• Project management: Experience managing research projects, including planning, execution, and reporting across multiple technical approaches.
• Mentorship: Ability to mentor and supervise junior researchers and PhD students, fostering a collaborative research environment that encourages exploration of diverse computational approaches.
• Innovation: A creative mindset with a passion for exploring and implementing innovative research ideas in alternative computing systems.
• Independence: Ability to work independently and take initiative in driving research projects forward across different computational domains.
• Time management: Excellent organisational skills, with the ability to manage multiple tasks and deadlines effectively while working with diverse technologies and approaches.

Desired requirements:

• Hardware-software co-design: Experience in optimising algorithms for specific hardware platforms or developing software-hardware integrated solutions.
• Heterogeneous computing systems: Experience with heterogeneous computing platforms, including GPUs, FPGAs, neuromorphic chips, or other specialised hardware accelerators is highly desirable.
• Cross-disciplinary experience: Background spanning multiple computational domains or experience translating approaches between different computing paradigms.

What we can offer you:

• The opportunity to work closely with the PI Dr Danyal Akarca (Imperial) and team, which includes Prof Daniel Goodman (Imperial) and Dr Jascha Achterberg (Oxford).
• The opportunity to work on one of ARIA’s first funded projects.
• The opportunity to continue your career at a world-leading institution and be part of our mission to continue science for humanity.
• Grow your career: Gain access to Imperial’s sector-leading dedicated career support for researchers as well as opportunities for promotion and progression
• Sector-leading salary and remuneration package (including 39 days off a year and generous pension schemes).