Postdoc “Machine-Based mapping of Innovation processes”
The Storylab at the Department of Psychology, Health and Technology of the University of Twente in the Netherlands is looking for a
Postdoc researcher in Natural Language Processing/machine learning
In the area of innovation management and business intelligence
Commercial innovation is vital for the future of organisations. Especially to large and mature corporations, managing innovation has become a great challenge, involving large numbers of teams inside and outside organisational boundaries, operating in various timeframes. Our current understanding of innovation processes and how they can be managed does not adequately match today’s reality. In this study, we will develop new and more accurate complex innovation process models by exploiting advanced machine learning and natural language processing techniques, applied to the empirical textual data of a large number of cases over an extended period of time. For this purpose, the Storylab seeks an ambitious NLP/machine learning researcher to strengthen our innovative eScience research programme.
The Storylab https://www.utwente.nl/igs/ehealth/research/story-lab/ is a Dutch multidisciplinary research centre specialized in technology-enhanced narrative research, and pioneering in text mining/machine learning approaches to change processes. The Storylab is based at the Department of Psychology, Health, and Technology, and currently in the process of widening its disciplinary scope from narrative psychology and e-health to e-humanities and management sciences. It combines a scientific orientation to the international research community with a practice-based focus evidenced by collaborations with local organisations. The Storylab is strongly embedded in the technical university at large and recently received a large grant from the e-Science Centre in Amsterdam.
You conduct research (0,9 fte) in the emerging field of text mining in innovation management. You will be part of a multidisciplinary team, and participate actively in building a UT text mining consortium across University departments (0,1 fte) to lay the foundation for a larger grant application. We are looking for a team player, but also someone who can execute the study without much supervision. The project consists of three phases: 1) supervised machine learning based on existing categories in hand-coded data; 2) unsupervised machine learning including a variety of textual features and connecting these to meta-data; 3) organization of a multi-disciplinary work conference aimed at interpreting the results from phase 1 and 2 from various theoretical and methodological points of view. Together with colleagues, you write 2 peer-reviewed articles based on the methods and results of the project.
The full document can be found here.