Posted to IASSIST on: 2017-07-10
Employer: University of Essex
Employer URL: http://www.essex.ac.uk/
This role works closely with the Director of the Institute and UNESCO Chair in Analytics and Data Science, Prof Maria Fasli, and provides senior academic leadership in promoting and supporting a range of research activities in analytics and data science. The successful candidate will be appointed to a permanent Academic post in the School of Computer Science and Electronic Engineering as a Senior Lecturer. You will be seconded to IADS as the Assistant Director for an initial period of three years, which may (subject to mutual agreement) be extended for a further period. The Institute is led by the Director, Professor Maria Fasli, who will linemanage the Assistant Director. You will also work closely with Professor Slava Mikhaylov, Chief Scientific Advisor to Essex County Council who is seconded from the University’s Department of Government to work on analytics and data science in one of the largest county councils in the UK, and who is based in IADS. During the secondment as Assistant Director of IADS, you will focus on undertaking research, developing research proposals, developing and undertaking collaborative projects with businesses, knowledge exchange, PG supervision and supporting analytics and data science short courses which run throughout the year. On completion of the secondment, the appointee will return to the School of Computer Science and Electronic Engineering, undertaking the normal range of education, research and leadership/citizenship duties of an Academic Staff with Education and Research (ASER) member of staff. As the Assistant Director 0.8 FTE of your time will be allocated to the Assistant Director role and 0.2.FTE allocated to your own research.
Duties of the Post: Assistant Director of Institute for Analytics and Data Science role
- Work together with the Director to provide senior academic leadership to shape and deliver research, business engagement, knowledge exchange, and professional development activities such as short courses within the Institute for Analytics and Data Science;
- Support existing research projects, knowledge exchange and training activities and the supervision and mentoring of research staff and students in areas of analytics and data science;
- Support IADS activities in research, education/training and knowledge exchange;
- Work together with the Director to further develop research capacity within the Institute;
- Champion data science as a key driver for developing knowledge economies and sustainable economic growth;
- Work with key internal and external stakeholders from within and outside the University to promote the University’s expertise in analytics, data science and big data and generate and develop new collaborations including research projects
- Collect and analyse data, prepare results and conclusions, write reports and present findings and recommendations for internal and external use;
- Promote and represent the University’s excellence in research and education and raise the University’s profile locally and nationally.
Education and research responsibilities
- Publish research work which is eligible for submission to the REF and other high quality scholarly publications that are not part of REF;
- Build research collaborations both internally and externally, and undertake internationally excellent research activity and produce high quality publications;
- Obtain external grant funding relevant to the discipline and level to support a sustainable research programme;
- Design and deliver a broad range of innovative teaching of excellent quality at both undergraduate and postgraduate levels;
- Supervision of Masters and PhD students, as well as undergraduate projects;
- Take on senior administrative and management roles in the Department.
Applicants should hold a PhD in Computer Science or related area and have excellent knowledge of computer science, artificial intelligence, machine learning or advanced text analytics. Particular research areas of interest include but are not limited to:
- machine learning;
- reinforcement learning, neural networks, deep learning;
- data mining;
- predictive analytics;
- natural language processing and advanced text analytics;
- semantic information extraction and the Semantic Web;
- social media analysis;
- handling of data in motion (multi-stream processing and reasoning, complex event processing and reasoning);
- decision support tools and systems.
Archived on: 2017-07-10