- a PhD degree (or equivalents) in Computer Science or a related field *
- As Research Fellow level you will be expected to have several first author publications in leading international scientific journals.
- Either a strong background in verification of neural networks, and adversarial search, or a strong background in machine learning with particular emphasis to robustness
- A good publication record in relevant conferences or journals.
- Excellent oral and written communication skills as well as good social skills.
- Ability to prioritize work to meet deadlines, and to work with minimal supervision.
- 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.
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Research Fellow
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2 weeks ago
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Research Fellow - London, United Kingdom - Imperial College London
Description
The at 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 Fellow(s) or Research Associate(s) to join the (VAS) group, led by . The VAS group is a friendly, vibrant, multi-national team working on various aspects of safe Artificial Intelligence, including verification and robustification of machine learning systems and autonomous systems. The group also has strong links with the UKRI Centre for Doctoral Training in Safe and Trusted Artificial Intelligence and US institutions.
The successful candidates will join the UKRI-funded project "Secure AI Assistants", jointly run by King's College London and Imperial College London. The overarching aim of the project is to develop various methods to assess the security of personal AI assistants, including identifying unwanted aspects of the system so that they can be rectified.
The role will focus on verification, robustification and adversarial attacks for AI assistants based on machine-learning, including transformer-based systems. Familiarity with existing methods for verification of neural networks, ., MILP-based methods, SAT-based methods, abstraction, and optimization is highly desirable, as well as methods for adversarial search and various forms of learning. Previous experience with neural networks used in NLP such as transformers or LSTM is advantageous but candidates demonstrating an ability and willingness to become familiar with these topics and able to contribute to them will also be considered.
Please see job description for a full list of requirements.
In addition to completing the online application candidate should attach.
For informal queries candidates are welcome to contact Professor Alessio Lomuscio