Postdoc in Ethics, Privacy and Fairness in Digital Education Environments
Cluster of Excellence: Machine Learning for Science, University of Tuebingen
|Job category||Postdoc or similar / Fixed term|
|Location||Tübingen, Baden-Württemberg, Germany|
Postdoc (m/f/d; E13 TV-L, 100%) in Ethics, Privacy and Fairness in Digital Education Environments
to be filled in Summer/Fall of 2021. The position is limited to three years.
The application of ML methods in digital education raises significant ethical issues. Adaptive learning systems promise to be particularly useful for disadvantaged students without adequate family support and could thus contribute to the reduction of educational inequalities. Modern machine learning techniques promise a revolution in interactive and personalized education. However, students who stand to benefit the most are also the least able to advocate for themselves. Moreover, irresponsible implementation of algorithmic systems threatens to lower education quality and widen existing inequalities. Accordingly, the Innovation Fund “Machine Learning in Education” in the Cluster of Excellence “Machine Learning: New Perspectives for Science” in collaboration with the Hector Research Institute of Education Sciences and Psychology seeks to hire a Postdoc for fundamental research in the ethics and methodology of machine learning for education.
The postdoc position (E13 TV-L, 100% - 36 Months) is to be filled (ideally) in Summer/Fall of 2021 and will be supervised by Konstantin Genin, Thomas Grote, Benjamin Nagengast and Bob Williamson. Close collaboration with the other members of the Innovation Fund “Machine Learning in Education” is expected. The position is funded for 3 years. Compensation is at minimum €4002/month brutto (€2379 netto) and increases according to experience. Funding for equipment, travel and other expenses is also available. Possible research areas include but are not limited to the following:
The position is, by its nature, extremely interdisciplinary. Therefore, we are open-minded about the background of potential applicants. Applicants holding a PhD in philosophy (esp. ethics), statistics, machine learning, social science (e.g. psychology, psychometrics, economics, political science, sociology), education or allied fields are welcome to apply. The postdoc will be expected to collaborate with other groups in the “Machine Learning in Education” Innovation fund on issues of ethics and methodology.
Please upload the usual documents (cover letter; short (1 page) research proposal; academic CV including list of publications; writing sample and letters, if available) as a single PDF to the indicated dropbox folder by the deadline of June 30, 2021. Please indicate in the cover letter which of your publications you would most like us to read and why you believe it is your best work. The group aims to decide on candidates by the end of the Summer. Questions can be directed to firstname.lastname@example.org.
The University aims to increase the proportion of women in research and teaching and therefore urges suitably qualified women scientists to apply. The “Machine Learning in Education” group also welcomes applications from other groups underrepresented in philosophy and machine learning. Qualified international researchers are expressly invited to apply. Equally qualified applicants with disabilities will be given preference. The employment will be carried out by the central administration of the University of Tübingen.
|How to apply|
|Web address to apply||https://www.dropbox.com/request/ga0JIO05mseXXROe3q...|
|Hard deadline||June 30, 2021, 11:59pm CET|
|Web address for more information||https://uni-tuebingen.de/en/128980#c1352461|
|Contact name||Konstantin Genin|
|Time created||June 1, 2021, 10:28pm UTC|
|Scheduled expiry date||June 30, 2021, 11:59pm CET|
|Last updated||June 1, 2021, 10:28pm UTC|
|Last update notification||
There are no notifications for this ad.
Save the ad using the "save" button below to receive notifications of significant updates.
|Job Market Calendar||
This institution has indicated that the position advertised will not follow the APA's recommended job market calendar. An explanation, if provided, appears below.
Hiring on an ongoing basis.