About the role We are excited to announce an opening for a Lecturer to join our School of Computing and Mathematical Sciences. 

You will be part of the Distributed and High-Performance Systems Research Group, led by Prof. Ashiq Anjum. This group currently comprises two professors, three assistant professors and several PDRAs and PhD students, who work in a variety of areas including digital twins, HPC, distributed ledger technology, process scheduling in data centres etc. The group is particularly strong in the area of digital twins and has a strong track record in obtaining funding, including co-leading a UKRI AI for Net Zero hub on Self-Learning Digital Twins for Sustainable Land Management. The group works closely with a number of partners across the university, including Space Park Leicester, the Leicester Institute for Environmental Futures, and the Leicester Institute for Precision Health.

The successful candidate will be expected to engage in high-level research and teaching in Computer Science, with a focus on research publication, teaching material development, research supervision, and generating impactful research through grants, consultancy, and knowledge transfer.

At the University of Leicester, you'll work in a supportive environment with research and teaching mentoring. Our research-intensive university is located at the heart of the UK, where different areas of activity are balanced by a workload model to ensure fair and transparent balance between staff and different areas of activity. About you We are looking for an enthusiastic educator capable of inspiring interest, curiosity and drive for learning in our students. You will have a real passion for the subject matter and be motivated to provide the very best experience for our students using your expertise and skill to ensure all reach their potential.

We are interested in candidates with a PhD and/or postdoctoral experience in the following areas:

Distributed machine learning algorithms and system architectures Methods for exploiting modern system architectures for high-performance AI Methods for embedding machine learning in large-scale deployment of distributed cyber-physical systems, particularly those comprised of components that operate autonomously Methods to meet performance, latency, scalability and complexity demands of distributed machine learning architectures  Novel digital twins platforms that integrate real-world constraints to offer reliable and trust-aware machine learning models. Quantum distributed computing, quantum machine learning

You should have an established reputation for research, with a high quality publication profile and be able to evidence your potentially fundable research ideas. You must also have excellent networking skills, which you use to seek out opportunities for collaboration and citizenship, both internally and externally.