University Assistant (predoc): Knowledge Graph-based GeoEnrichment for Environmental Intelligence
Supervision: Krzysztof Janowicz (Department of Geography and Regional Research), in collaboration with Yingjie Hu (University at Buffalo, USA), Mark Schildhauer (NCEAS, USA), Song Gao (University of Wisconsin-Madison, USA)
Project outline:
Environmental intelligence delivers insights into how our changing environment affects society, e.g., by helping decision-makers to increase the resilience of food supply chains in the presence of extreme events. To use environmental intelligence, decision-makers and data scientists must rapidly incorporate highly diverse data into their analytical frameworks to contextualize their (local) knowledge with auxiliary data (from the cloud) at the intersection between humans and the environment. In many regards, data diversity is more important for environmental intelligence than the size of individual datasets alone. This puts data integration and interoperability at the forefront of data-driven decision-making. One key technology to deliver such pre-integrated, AI-ready data on-demand are so-called knowledge graphs. Instead of merely considering properties, these graphs focus on the connections between places, people, events (e.g., disasters), news, and so on. Unfortunately, the question of how to integrate knowledge graphs into the established workflows of decision-makers, and more specifically into their Geographic Information Systems (GIS), is underexplored. While GIS systems offer geo-enrichment services to provide cloud-based access to pre-apportioned data, these services work on well-curated tabular data, not on evolving, highly-integrated, and global knowledge graphs containing thousands of interconnected statements about any region on the surface of the Earth. Together with intentional partners in academia and industry, this project will study how geo-enrichment can be performed on top of geographic knowledge graphs, how to identify graph statements relevant to a given region, and how to use machine learning techniques to help guide decision-makers in exploring these vast graphs.
Job Description:
The position aims to deepen and extend the professional and scientific education targeting a doctoral degree. Tasks and responsibilities include:
● Independent research and development of an academic profile targeting a doctoral degree. We expect the successful candidate to sign a doctoral thesis agreement within 12 months.
● Participation in research, teaching and administration of the research group/department
● Participation in publications / academic articles / presentations
● Participation in teaching and independent teaching of courses as defined by the collective agreement
● Supervision of students
● Participation in evaluation activities and in quality assurance
● Involvement in the organisation of conferences, workshops and (project) meetings
The candidate who is selected for this position joins VISESS as a PhD student member.
Profile:
● Astronomy / Earth Sciences / Geography / Geoinformatics / Environmental Sciences: Master Degree or equivalent
● Excellent command of written and spoken English
● Experience with (geographic) knowledge graphs and knowledge representation, including work on graph embeddings
● Strong GIScience/ Geoinformatics skills incl. programming skills
Expected starting date: Ideally June 2022 or later
Duration of employment: 3 years with the possibility of extensions up to 4 years. The employment relationship is initially limited to 1.5 years and automatically extended to a total of 3 years unless the employer submits a declaration of non-renewal after a maximum of 12 months. In exceptional, well argumented cases and on the condition that budget is available, there is the possibility of extensions up to 4 years.
We offer:
● Job grading in accordance with collective bargaining agreement: §48 VwGr. B1 Grundstufe (praedoc) with relevant work experience determining the assignment to a particular salary grade
● An active and inspiring research environment, a vibrant PhD community and many ways to connect with peers from home and abroad on a social and professional level
● A broad range of interdisciplinary training possibilities and school activities, such as leadership skill trainings, workshops, seminars, mobility and summer schools
Extent of employment: 30 hours/week
Application: Interested students can apply until the deadline of 15 April 2022 via: <https://visess.univie.ac.at/how-to-apply/>.
Further information: For further information, please contact <admin.visess@univie.ac.at>.
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.