Postdoctoral Research Fellow: Automated Text Analysis
University of Michigan - Ann Arbor
April 14, 2018
Ann Arbor, Michigan
Academic/Faculty, Other - Academic/Faculty
A cover letter is required for consideration for this position and should be included as the first page of your CV. The cover letter should outline your specific interest in the position and outline skills and experience that directly relates to this position. Please apply online at https://umjobs.org/, job opening number 153763.
A generously funded project (2016-2021) welcomes applicants with interest in and expertise in automated text analysis. The project, M-Write, brings together researchers from writing studies, the natural sciences, and computer programming to investigate how Writing-to-Learn pedagogies promote conceptual learning by students in large-enrollment gateway courses. Students write in response to carefully crafted assignments, participate in automated peer review, and then revise their drafts. Approximately 10,000 students have already participated in M-Write courses, which means that there is already a large corpus of student writing and peer review responses, with more being added each semester. We seek a specialist in automated text analysis to join the research team. Goals include principled corpus compilation, analysis, and development of corpus-driven algorithms to support student learning of key concepts highlighted in assignments.
This position will be a 12-month Post-Doctoral Fellowship beginning in June 2018 with possibility of renewal. The salary is negotiable.
Retrieve and create corpora for NLP and associated linguistic analysis
Develop and test systems for identifying students who appear to lack key concepts as defined by lexical and grammatical structures, discourse analysis, and possible sentiment analysis
Design a data warehouse that will facilitate the ability to collect, manage, and archive data including student interviews, student surveys, student writing samples, and academic and demographic data for students in order to conduct research leading to peer-reviewed publications, and future external funding
In collaboration with other project staff collect, manage, and archive data including student interviews, student surveys, student writing samples, and academic and demographic data for students in order to conduct research that could lead to peer-reviewed publications
Organize, synthesize, and analyze project data, and write co-authored papers with project PIs for peer-reviewed articles
Ph.D. in computational linguistics, natural language processing, machine learning, cognitive linguistics, sentiment analysis or related area. Previous research experience in textual analysis in terms of syntax (e.g. parts of speech tagging), semantics (e.g. topic segmentation) and discourse analysis is essential. Research in statistical modeling is also required. Strong computer science skills are essential, and data warehouse design skills are highly desired. Background in the theory and research of writing-to-learn pedagogies and experience with educational technology in natural language processing are desired but not required.
The University of Michigan conducts background checks on all job candidates upon acceptance of a contingent offer and may use a third party administrator to conduct background checks. Background checks will be performed in compliance with the Fair Credit Reporting Act.
The University of Michigan is an equal opportunity/affirmative action employer.
Internal Number: 153763
About University of Michigan - Ann Arbor
A great university is made so by its faculty and staff, and Michigan is recognized as one of the best universities to work for in the country. The Michigan culture is known for engaging faculty and staff in all facets of the university to create a workplace that is vibrant and stimulating.For two consecutive years, the Chronicle of Higher Education has placed U-M in its "Great Colleges to Work For" survey. In particular, the university earns high marks for strong relations between faculty and administrators, a collaborative system of governance, strong pay and benefits, and a healthy work/life balance.