PhD Openings

For further information on openings for PhD students to join our group, please visit our PhD Opportunities page.

Postdoc Openings

Please refer to the descriptions below for more details.

Research Assistant/Associate in Neural Network Verification for Complex Event Forecasting

We are currently recruiting a Research Associate in Neural Network Verification for Complex Event Forecasting.

Further information on the job and an online application form can be found below.

  • Salary range: £38,194 to £50,834 per annum*
  • Closing date: 4 April 2023
  • Location: White City Campus - On site only

Job summary

The Department of Computing at Imperial College London is a leading department of computer science, with a strong international presence in verification and artificial intelligence.

We are seeking to hire an outstanding Research Assistant/Associate to join the Verification of Autonomous Systems group, led by Prof. Alessio Lomuscio. The VAS group is a vibrant, multi-national team working on various aspects of Safe Artificial Intelligence, including verification and safety aspects of deep learning systems, autonomous systems and beyond. The group has strong links with the UKRI Centre for Doctoral Training in Safe and Trusted Artificial Intelligence and several ongoing collaborations with the industry. The Department of Computing at Imperial College London is a leading department of computer science, with a strong international presence in verification and artificial intelligence.

Duties and responsibilities

The role will focus on the application and development of methods for neural network verification, focusing on deep learning systems suitable for event prediction. Aspects of explainability, safety and reliability will also be pursued. The successful applicant will join the Horizon Europe project “EVENFLOW”, contributing to the realisation of methods and tools to be adopted project-wide and running proof-of-concepts on use cases with selected project partners. The overarching aim of the project concerns the development of techniques for the verifiable forecasting of complex events in highly distributed and uncertain environments. The envisioned methods will combine neural representation learning with symbolic reasoning tools (neuro-symbolic learning).

The successful candidate will have a background in deep learning, verification, or optimization. Familiarity with existing methods for neural network verification (e.g., methods based on optimization, MILP, SAT, or abstraction) is desirable, but candidates demonstrating an ability and willingness to become familiar with these topics and able to contribute to them will also be considered. Working knowledge of neural networks and their training is highly desirable as is some experience or willingness to learn methods and tools for high-performance event-based systems.

Essential requirements

  • Hold (or shortly expect to receive) a PhD degree in Computer Science or a related field
  • Either a strong background in verification methods of neural networks, eg MILP-based methods, SAT-based methods, abstraction, and optimisation, or in machine learning with particular emphasis on robustness.
  • A good publication record in relevant conferences or journals.
  • Excellent oral and written communication skills as well as good social skills.
  • Ability to prioritise work to meet deadlines, and to work with minimal supervision.

Further information

*Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant within the salary range £36,694 - £39,888 per annum.

How to apply

For further details on this opportunity visit this link.

In addition to completing the online application candidate should attach:

  • A full CV
  • Up to 1 page statement explaining what interesting issues the candidate sees in the above post and the reasons why his or her expertise is relevant.

We will be reviewing applications on a rolling basis and reserve the right to close the advert early if the position is filled. It is advisable that you submit your application as early as possible.

For informal queries candidates are welcome to contact Professor Alessio Lomuscio:

For queries regarding the application process contact Jamie Perrins: