We are seeking to hire a Research Assistant (Pre-Doc) to join the research group of Dr Giuliano Casale in the Department of Computing and the f-inference project. f-inference aims to shift from a centralised, cloud-based deployment of Foundation Models (FMs) towards a Resource-driven Computing Continuum (RCC): a layered architecture that seamlessly spans Cloud, Edge and IoT tiers, deploying intelligence as close as possible to data sources and end users for a distributed, adaptive, sovereign, robust and energy-efficient paradigm of AI deployment.
Imperial College is consistently in the top 10 world university rankings with the Department of Computing ranked top of the 2021 UK REF assessment.
This position will carry out research into the performance, cost and reliability modelling of distributed Foundation Model inference across the cloud continuum, spanning two main tasks. Firstly, the post-holder will develop simulation-based and analytical methods to evaluate adaptive caching policies for distributed FM architectures based on KV stores, quantifying the cost and latency of the caching infrastructure and its sensitivity to replacement, replication and time-to-live (TTL) configurations, and studying backpressure and other control mechanisms to mitigate hotspots. Secondly, the post-holder will also contribute ICL’s cost and reliability modelling for distributed, layered systems, employing stochastic and multi-layer network models to characterise disruptions of expected latency and throughput, and recovery under failures, attacks or other reliability-related events. The research will involve building and validating such models in simulation and experiments, but this is driven from the fundamental theoretical knowledge that underpins them, therefore contributions to knowledge come primarily from the modelling and AI-systems elements of the project.
This position presents a novel opportunity to work alongside Dr Giuliano Casale and the f-inference consortium in realising their full potential and planning for the future. f-inference is a close collaboration of academics and researchers from across Europe as well as partners from industry.
- A first / Masters degree (or equivalent) in Computer Science or a closely related discipline.
- Proven research in performance, cost and reliability modelling of distributed and cloud/edge systems, or in AI / Foundation Model inference serving, with
- Demonstrations of the ability to build proofs-of-concept of such systems
- Practical Understanding of methods and techniques relevant to the role including: Performance & reliability modelling , LLM inference serving and cost optimisation, Resource scheduling, Libraries & Frameworks: Python, PyTorch, NumPy, SciPy, Pandas, discrete-event simulation, Anaconda, Jupyter, Systems evaluation: statistical analysis and performance metrics (e.g., latency, throughput, energy, accuracy).
- Strong software engineering skills to build simulation tools and prototypes will be an advantage.
You will have the opportunity to continue your career at a world-leading institution. Imperial College is currently ranked #2 in the QS world university rankings with the Department of Computing ranked top of the 2021 UK REF assessment.
You will receive a sector-leading salary and remuneration package (including 39 days off a year) and a comprehensive early career development support package including 10 training and development days.
Please see job description for full list of requirements.
Full-time, Fixed term to start October 2026 for 3 years with potential extension.
In addition to completing the online application candidates should attach:
- A full CV, highlighting publications or preprints relevant to the position
- A 1-page research statement on how your experience meets the above requirements.
If you require any further details on the role, please contact Dr Giuliano Casale: [email protected]
For queries regarding the application process contact Jamie Perrins: [email protected]
