Cyber Security Posts in Computer Science at Aston

Despite what it currently says here: https://www.jobs.ac.uk/job/BXB915/academic-appointment-in-cyber-security-at-any-level-including-chair, we have several posts in Cyber Security. The information about the posts is copied below:

Aston University’s mission is to be the UK’s leading university for students aspiring to succeed in business and the professions where original research, enterprise and inspiring teaching deliver local and global impact. Aston University was awarded Gold in the 2017 Teaching Excellence Framework. We have developed a bold strategy around our beneficiaries because their future success is important to us. We define our beneficiaries as those who the University exists to serve; students, external organisations, and our region and society.

The School of Engineering and Applied Science (EAS) has a tradition of quality and reputation for cutting edge research, offering students excellent opportunities to join in the process of discovery and creativity, and prepare for an increasingly diverse and technological world. EAS was recently awarded the Silver Athena Swan Award in 2018. One of the academic groups within EAS is Computer Science, which focuses on creatively solving the problems of business and industry through advanced software engineering. The group’s strengths include software engineering and process, distributed computing, knowledge representation and engineering, pattern analysis, AI, robotics, machine learning, natural language processing, HCI, computing education and digital health. We also deliver distinctive and innovative programmes at undergraduate and Masters levels that achieve some of the highest employability rates for Computer Science graduates in the UK.

We are now seeking to appoint a full-time academic in cyber security at any level including Chair to develop and contribute to an internationally-recognised research programme in Cyber Security or closely-related field, that has significant impact in the field. S/he will also contribute to the development of the new BSc in Cyber Security and contribute to the delivery of teaching as part of the BSc in Cyber Security, and potentially other Computer Science programmes. A PhD in Computer Science or related discipline is essential. The appointee will have a proven track record of sustainable research in cyber security or closely-related discipline. S/he will have a track record in successfully securing research funding, experience of engaging with industry partners and have considerable teaching experience with evidence of module or programme development, innovation and good student satisfaction. Applications from individuals with senior technical roles in cyber security in industry or business will also be considered.

For further details, including job description, person specification and information on how to apply, please see https://candidates.perrettlaver.com/vacancies/ quoting reference 4192. For informal enquiries about the role, please contact Nataliya Mykhalchenko via email at Nataliya.Mykhalchenko@perrettlaver.com or on +44 (0)20 7340 6237.

The closing date for applications is midday on Sunday 2^nd February 2020.

Popularity of Programming Languages

Fascinating video showing the popularity of programming languages over the years:

Reinforcement Q-Learning in ESL

The language ESL merges Actors with Function Programming and other features to make the construction of highly concurrent applications as easy as possible. ESL is currently in development and we are developing support for Machine Learning, and Reinforcement Learning in particular.

A form of Reinforcement Learning is called Q-Learning (described here). A simple example is a Predator-Prey situation in a grid-world where a predator can move horizontally and vertically in the grid and must navigate to a randomly placed prey. To start off, the predator has no knowledge of how to do this, it just moves randomly in the world. When it catches the prey, the predator gets a reward. Q-Learning will then back-up the reward along the path that led to the goal being achieved thereby reinforcing that policy the next time it is encountered but he predator. If this is repeated a sufficient number of times, the resulting policy will allow the predator to catch the randomly placed prey every time, even though the hard-coded behaviour for the predator has no knowledge of such a strategy.

The video below shows three things:

1. An implementation of a generalised Q-Learning policy creation function mkPolicy in ESL.
2. A Predator-Prey example that consisting of a world and an independent predator actor. The predator actor uses mkPolicy to create a policy, and then supplies information to improve the policy.
3. A run of the example showing two phases: the learning phase followed by a stable phase. The learning phase starts the predator and prey in random positions and uses the current version of the policy (which is initially random) until the predator finds the prey. During the learning phase the policy is updated with information about the world and gradually improves. The stable phase does not change the policy: it just uses it. You can see that the learning phase involves lots of random movement (although it gets less random towards the end) and the stable phase is very directed.

To see the source code, you may need to halt the video play or you can download the files policy.esl and policy_pred.esl.