Postdoctoral Positions at Center for Data-Intensive Systems, Aalborg University, Denmark
 
The positions, available starting May 2017, cover the following topics:
 
Spatio-temporal analytics: The volumes of available spatio-temporal, and spatio-textual, data are increasing at a rapid pace. Examples of such data include GPS-trajectory data, web-content with location, geo-coded Tweets, and check-ins. In spatio-temporal analytics, we aim to provide efficient support for advanced traffic analytics such as highly accurate and personalized vehicle routing based on the current state of traffic as well as a variety of spatio-textual analytics such as ranking and clustering of spatio-textual content. Applicants within this topic are expected to join ongoing research activities.
 
Managing Big sensor data: Sensor data is being collected at an unprecedented speed and volume. Current technologies, even Big Data frameworks, cannot keep up with the volume, pace, and analytics requirements of such data.
This topic concerns building a scalable, efficient, and powerful platform for managing Big sensor data, primarily in the form of time series. The platform should employ advanced techniques, e.g., model-based storage, approximate query processing, and streaming, in order to enable advanced real-time analytics on Big sensor data.
 
Prescriptive analytics: Prescriptive Analytics does not only predict the future (like predictive analytics), but also suggests (prescribes) the best course of action to take, given constraints, objectives, requirements, and parameters for a given business optimization scenario. This supports effective decision making based on mathematical optimization and simulation. However, building prescriptive analytics solutions with current tools is labor-intensive, error-prone, and inefficient, as a range of non-integrated and specialized tools have to be glued together in an ad-hoc fashion. 
This topic concerns building a generic platform for prescriptive analytics for Big Data, which tightly integrates scalable data storage with declarative specification of queries, constraints, objectives, requirements, and mathematical optimizations in a unified framework which is both powerful, scalable, and efficient, yet easy-to-use.
 
Smart energy: the increasing deployment of renewable energy sources like wind and solar creates a big need for balancing energy supply with flexible energy demand at all levels of the grid, including prosumer, community, and market area levels. Daisy has pioneered the unique data-driven “flex-offer” approach, where energy flexibilities in amount, time, and/or price are explicitly captured, aggregated, and scheduled/optimized according to (forecasted) energy prices and grid capacities. 
This topic concerns developing scalable and effective techniques for (near) real-time trading of flexible energy in the form of flex-offers, including aspects of geo-localized flexibility pricing, micro-contracting, prediction, aggregation, optimization, and efficient distributed implementation, and integrating this into a powerful platform for automated demand response trading. 
For strong applicants without a Ph.D. degree, this specific position can also be filled at the level of scientific assistant. 
 
The positions are funded by the DiCyPS project (Innovation Fund Denmark) and the GOFLEX project (Horizon 2020), as well as by grants from the Obel Family Foundation and the VKR Foundation.
 
Requirements:
Applicants are expected to have a Ph.D. in computer science and an excellent scientific background and publication record within spatio-temporal data management, sensor data management, big data management/analytics, data mining, machine learning, mathematical optimization/operations research, and/or smart energy/smart grid technologies, depending on the particular position.
 
Employment:
The positions are initially available for 12 months with the possibility of extension up to 24 months based on mutual agreement. There are good opportunities to apply for assistant professor positions as a follow-up. The annual salary is ca. DKK 450.000 (ca. EUR 60,000, inclusive of pension
contributions and pre taxes). Light teaching assignments are possible.
 
Information and Application:
Please send your cover letter including topic(s) applied for, the preferred starting date, a research statement, contact information of two references, copies of certificates for all obtained degrees, a CV including a list of publications, and copies of at least two relevant publications in a single PDF file to
Christian S. Jensen csj@.cs.aau.dk (spatio-temporal analytics) or Torben Bach Pedersen tbp@cs.aau.dk (model-based data management, prescriptive analytics, smart energy).
 
Priority will be given to applications received before March 20. Evaluation will start immediately and continue until the positions are filled.
 
For further information and details, please contact Professor Christian S. Jensen csj@cs.aau.dk (spatio-temporal analytics) or Professor Torben Bach Pedersen tbp@cs.aau.dk (big sensor data, prescriptive analytics, and smart energy).
 
About Daisy:
The database researchers at Daisy conduct research in data-intensive systems, spatio-temporal data management, (big) data analytics and mining, and (semantic) web data management. International evaluations place Daisy in the global top tier. An independent study of publication performance in the top database outlets in the 10-year period 2001-2010 ranks Daisy second among all research groups in Europe. More information about Daisy can be found at http://daisy.aau.dk.
 
About Aalborg:
Aalborg is a lively city with numerous cultural attractions. The area features relatively low cost of living, clean air, beautiful forests and beaches, and very good transportation infrastructure. A recent report on European cities ranks Aalborg highest:
http://ec.europa.eu/regional_policy/sources/docgener/studies/pdf/urban/survey2013_en.pdf