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Scientific project staff (prae doc) at the Research Group Data Mining and Machine Learning at the Faculty of Computer Science
The University of Vienna (20 faculties and centres, 178 fields of study, 9.900 members of staff, about 89.000 students) seeks to fill the position of a
Scientific project staff (prae doc)
at the Research Group Data Mining and Machine Learning
at the Faculty of Computer Science, University of Vienna
under the supervision of Prof. Benjamin Roth
Reference number “#01: Scientific project staff, Data Mining & Machine Learning, Prof. Roth”
The Faculty of Computer Science of the University of Vienna has world-leading researchers in Computer Science who pursue basic as well as applied research. The UniVie Doctoral School Computer Science (DoCS) builds an essential framework to foster excellence in research and teaching. Its main focus are young prospective researchers eager to make an impact on both basic research as well as applied problems with collaborations across the University and beyond. The DoCS aims to provide these young researchers with the broad knowledge and expertise needed to perform Computer Science research at the highest achievable quality. The Doctoral School trains doctoral candidates in solving basic as well as applied research questions of high relevance.
The working group of Benjamin Roth is jointly located at the Research Group Data Mining and Machine Learning (Faculty of Computer Science), and the Faculty of Philological and Cultural Studies, with several interdisciplinary PhD and Postdoc positions in the areas of machine learning for natural language processing and computational linguistics.
This opening is a PhD position at the Faculty of Computer Science, and the candidate will work within the project “Knowledge-infused deep learning for natural language processing”.
Possible research topics are:
Combining formalized knowledge (e.g. linguistic knowledge, world knowledge) with data-driven methods
Weak supervision of natural language processing models
Extraction of structured information from text, linking knowledge graphs and language
Extraction of linguistic information and meta-attributes, such as authorship
Duration of employment: 3 years (with the possibility of extension to 4 years) Extent of employment: 30.0 hours/week Job grading in accordance with collective bargaining agreement: §48 VwGr. B1 Grundstufe (praedoc) (the gross monthly salary of the B1 salary group amounts to EUR 2,971.50) with relevant work experience determining the assignment to a particular salary grade. Starting date: Flexible
Participation in research and administration:
Participation in research projects / research studies
Participation in publications / academic articles / presentations
We expect the successful candidate to sign a doctoral thesis agreement within 12-18 months.
Involvement in the organisation of meetings, conferences, symposiums
Involvement in the department administration as well as in teaching and research administration
Master/diploma degree in Computer Science, or a related field
Strong background in mathematics, statistics and/or machine learning
Strong motivation to publish in top refereed conferences/journals
Strong interest in recent developments in deep learning and natural language processing
Good knowledge of a deep learning programming framework
Very good command of written and spoken English
Positive attitude, strong motivation to work with colleagues and students
Letter of Motivation including ideas for a prospective doctoral project proposal
Degree certificates, and transcripts of records
List of publications, evidence of teaching experience (if available)
Example of scientific writing (e.g., project report, BSc. thesis, MSc. thesis, publication)
Abstract of master thesis
Contact details of two referees who can provide a letter of reference
For further information please contact Benjamin Roth (+43-1-4277-79513).
The University pursues a non-discriminatory employment policy and values equal opportunities, as well as diversity (http://diversity.univie.ac.at/). The University lays special emphasis on increasing the number of women in senior and in academic positions. Given equal qualifications, preference will be given to female applicants.
The candidate who is selected for this position joins the DoCS as doctoral student member.