Every year, the Independent Research Fund Denmark (DFF) awards a number of grants to some of Denmark's most promising research leader talents, through the Sapere Aude programme: Independent Research Fund Denmark - Research Leaders. This year, the Independent Research Fund Denmark has appointed a total of 36 young research leaders to venture out and conduct research at a high international level with their own research team. A total of seven researchers from AU have received an Independent Research Fund Denmark Research Leader grant, and three come from NAT.

Clean water

Assistant Professor Denys James Grombacher from the Department of Geoscience has received DKK 6.1 million for his project "Finding drinking water in data-poor regions". Clean water is a necessity for everyone, but all over the world many people have to use untreated water from surface sources and water holes, with the consequent risk of contamination. The project will develop and utilise NMR technology and it will culminate in a number of localisations of safe aquifers in vulnerable African villages and refugee camps.

Calculation of sensitive data

Assistant Professor Peter Scholl from the Department of Computer Science focuses on sensitive data that is found everywhere in our daily lives, from our bank transactions to our online social interactions and even our medical records. Cryptography is an important tool in protecting data against prying eyes. However, traditional cryptography only protects data when it is saved or during transit, and not when it is being processed. The project "New Directions in Cryptographic Computing and Correlated Pseudorandomness (C3PO)" has received DKK 6.1 million to develop modern cryptographic techniques known as 'secure calculation', which makes it possible to process data, even when it is encrypted, so that only the desired result of the calculation is revealed.

Optimal strategies

Associate Professor Claudia Strauch from the Department of Mathematics takes her outset in a question from stochastic control theory. The general problem can be formulated as follows: How can I control a system that is affected by randomness to maximize my return? The answer is not straightforward, but with a grant of DKK 5.7 million, the project "Learning diffusion dynamics and strategies for optimal control" is now trying to apply methods from statistics and state-of-the-art machine-learning techniques to develop both a general theory and specific methods that combine knowledge and results from the fields of non-parametric statistics, machine learning, and stochastic control.