FACULTY POSITIONS AT THE UNIVERSITY OF EDINBURGH Lecturer/Senior Lecturer/Reader in Natural Language Processing Lecturer/Senior Lecturer/Reader in Computational Social Science
Applications are invited for two faculty positions in Natural Language Processing and Computational Social Science in the School of Informatics at the University of Edinburgh. The posts are based within the School's Institute for Language, Cognition and Computation but potentially have links to other Institutes and Schools. The successful candidates will have (or be near to completing) a PhD, an established research agenda and the enthusiasm and ability to undertake original research, to lead a research group, and to engage with teaching and academic supervision. We are seeking current and future leaders in the field. We currently have two positions available however may wish to recruit to additional positions in the case of exceptional applicants. Appointment will be full-time and open-ended. All applications must contain the following supporting documents: * Teaching statement * Research statement * Full CV (resume) and publication list Please note that all posts close 5pm UK time on 22 January 2020. Lecturer Grade: UE08 (GBP 41,526 - GBP 49,553) Senior Lecturer or Reader Grade: UE09 (GBP 52,559 - GBP 59,135) 1. Natural Language Processing The School of Informatics is seeking to extend its teaching and research in natural language processing. Candidates with interest in the following areas are particularly encouraged to apply, but applicants in all areas of NLP will be considered: * machine-learning centric NLP * unsupervised learning for NLP * NLP for explainable AI * conversational systems * human-robot interaction * multimodal NLP (e.g., language and vision) * machine translation * ethics, bias, and fairness in NLP * NLP and knowledge bases The School of Informatics is one of the top places in the world for research in natural language processing, and benefits from strong links to Linguistics, Psychology, Social and Political Sciences, and Design. The present post has been created to reinforce Edinburgh's strength in NLP and to foster inter-disciplinary research, knowledge exchange and teaching. 2. Computational Social Science The School of Informatics is seeking to extend its teaching and research in computational social science. We seek candidates with an interdisciplinary research profile, who use methods from informatics and computer science (e.g., network analysis, machine learning and data mining, agent-based systems) to study social attitudes, behaviour, or other social phenomena. Candidates with interest in big social data analysis, graph/web mining, web science, or natural language processing applications for social science are particularly encouraged to apply. Candidates are expected to have a strong track record of publications in leading conferences in computational social science, such as WWW, CSCW, ICWSM, WebSci, CHI, and in top-tier journals in the field. Candidates should be comfortable contributing to core teaching in computer science and AI (e.g., machine learning, algorithms and data structures) as well as delivering more specialized topics (e.g., social network analysis, text mining for social media, ethical issues of big social data). The successful candidate will help to build stronger ties between the School of Informatics and the School of Social and Political Science, and will contribute to the School's growing commitment to computing for social good. Relevant University-wide initiatives include the recently established Centre for Ethics of Data and AI, part of the Edinburgh Futures Institute. Further particulars for the two posts can be found at: https://www.vacancies.ed.ac.uk/pls/corehrrecruit/erq_jobspec_version_4.jobspec?p_id=050521 https://www.vacancies.ed.ac.uk/pls/corehrrecruit/erq_jobspec_version_4.jobspec?p_id=050522 Informal enquiries may be addressed to Prof Frank Keller ([email protected]). _______________________________________________ Mt-list site list [email protected] http://lists.eamt.org/mailman/listinfo/mt-list
