SICSA continues to grow as a world-class pool of researchers in Informatics and Computing Science and there are a range of vacancies occurring regularly across the SICSA member Universities.

This page also features vacancies from industrial employers seeking to recruit graduates in Informatics and Computing Science. Please note that all applications to external vacancies (outside SICSA) must be made directly to the advertising institution or organisation and not via SICSA.

Director of SICSA

Applications are invited from any suitable member of academic staff within a SICSA Institution for the role of Director of SICSA.  

Job Purpose
SICSA is the Scottish Funding Council Research Pool in Informatics and Computer Science.  The goal of SICSA is to cohere the Scottish Informatics and Computer Science research communities to help increase critical mass and to enable cooperation in research, teaching and knowledge exchange.  The SICSA Director provides academic leadership for SICSA, working with the SICSA Directorate and the SICSA Executive.

Research pooling across Scotland is entering an exciting phase. Having recently released the review of pooling, the SFC are considering a number of recommendations which present opportunities for someone interested this role. You can lead and shape the foundations for the future of SICSA as we develop into a new programme for Scottish research. Anyone interesting in taking on this role should be willing to lead and work with other members of the directorate on developing a new direction for SICSA in light of a changing funding landscape.

The SICSA Director will be funded by SICSA at 0.2FTE, capped at £20K

Main Responsibilities
The SICSA Director role is part-time, taking approximately 20% of the working week as follows:

  • Working with Directors and SICSA Executive to deliver activities such as research themes, researcher mobility, SICSA Graduate Academy, Education, Knowledge Exchange and other events (10%)
  • Directing and Chairing the SICSA Committee, SICSA Research Committee and SICSA Advisory Board, interacting with SICSA constituent Schools. (2%)
  • Liaison with Scottish Funding Council (SFC) and other bodies relative to the strategic direction of SICSA.  Representing SICSA on various external boards. (2%)
  • Developing overall SICSA strategy with the other SICSA Directors in the areas of research, teaching and knowledge exchange. (3%)
  • Representing and presenting SICSA at events such as DemoFest, SICSA PhD Conference, and other external events. (2%)
  • Participate in the annual Performance & Development process for the SICSA Executive Officer. (<1%)

If you would like to speak informally about the role, please contact the current SICSA Director, Professor Aaron Quigley (

Full details on the role can be found on the Director of SICSA Job Description
To apply for the role please complete the Director of SICSA Application Form and send to along with an up to date CV.

Due to the current situation the application closing date has been extended to 08 April 2020

University of Aberdeen, Funded PhD Project: Self-Supervised Learning and Variational Inference for adaptation in Deep Learning


Hours: Full time
Contract: 3-year fully funded PhD project
Salary: Tuition Fee waiver at UK/EU rates and stipend paid monthly in arrears (for 2019/2020 = £15,009)
Closing Date: 12 noon on 1st of June 2020
Starting date: 1st of October 2020 or as soon as possible thereafter


We are looking for a highly motivated prospective PhD student to undergo a 3-year fully funded PhD project on Machine/Deep Learning foundations/theory and applications within the Department of Computing Science at the University of Aberdeen (UoA).

UoA is one of the oldest Universities in the UK with a strong focus on Artificial Intelligence, and consistently ranks among the top 200 Universities in the world.

Deep Learning theory has seen an immense development in the past few years across a number of areas, such as convolutional neural networks, capsule networks [1], generative adversarial networks, Bayesian deep learning [2], etc. However, some open problems such as improving predictive performance whilst reducing complexity, learning with few examples, explaining decisions, estimating uncertainty, improving routing and scaling in capsule networks are some areas than further investigations are needed to pass onto the next phase of deep/machine learning research.

This project aims at developing novel techniques in the areas of self-supervised learning, variational inference and domain adaptation, especially when it comes to dealing with few examples in real-world datasets that might be noisy, along with incorporating uncertainty for more explainable and effective decision-making process.

The idea of self-supervised learning is that one can aim at exploiting knowledge within the available data beyond the task at hand, i.e. enhancing the representation learning process via introducing pretext tasks, which can enhance the learning process and potentially the adaptation to other domains. In addition, many real-world applications require some sort of transparency in the decision-making process, therefore incorporating variational inference properties can be important to gaining trust on the deep learning outputs.

Stemming from the PI’s collaborations and current research activity, this PhD project will align and apply new theory in impactful application areas that include but are not limited to environmental datasets and energy [3], Oil and Gas industry (e.g. gas turbines) [4], nuclear reactors [3] and food industry [5].

There is some scope to shape the exact theoretical focus of this PhD project and title to align with the interests and background of the prospective student as well as the various collaborators that could be included in the project, such as the National Decommissioning Centre, Centre for Ecology and Hydrology and University of Lincoln.

You will work closely with other researchers and PhD students within Dr Leontidis’s lab on Applied Machine Learning, as well as other areas of the University that are considered of high strategic importance.

Selection will be made on the basis of academic merit. The successful candidate should have, or expect to obtain, a UK Honours degree at 2.1 or above (or equivalent) in Computer Science, Mathematics or Engineering.

Highly desirable – MSc (or near completion) in Machine Learning, Artificial Intelligence or Computer Science

Knowledge of: Machine Learning, Deep Neural Networks, Mathematics, Programming in Python.
Knowledge of Tensorflow, Keras, Pytorch and/or other deep learning frameworks would be advantageous


• Apply for the Degree of Doctor of Philosophy in Computing Science
• State the name of the lead supervisor as the Name of Proposed Supervisor
• State the exact project title on the application form

Please include the following documentation when you apply:

*BSc and MSc Degree Certificates and Academic Transcripts
*2 Academic References
*Detailed CV
*Detailed personal statement indicating why you are interested in the project

Closing date for applications is 12 noon on 1st of June 2020, but we reserve the right to close the advert earlier should a suitable candidate be found.

Starting date: 1st of October 2020 or as soon as possible thereafter

Funding Notes

Tuition Fee waiver only, provided at UK/EU rates and stipend paid monthly in arrears (for 2019/2020 = £15,009). International students are welcome to apply, providing they can meet the difference between UK/EU and International tuition fees (2019/2020 = £15,680 per annum) from their own resources for the duration of study.

Find more details here.