Doctoral student positions in Machine Learning over Networks at KTH Royal Institute of Technology

KTH Royal Institute of Technology in Stockholm has grown to become one of Europe’s leading technical and engineering universities, as well as a key centre of intellectual talent and innovation. We are Sweden’s largest technical research and learning institution and home to students, researchers and faculty from around the world. Our research and education covers a wide area including natural sciences and all branches of engineering, as well as architecture, industrial management, urban planning, history and philosophy.

The School of Electrical Engineering and Computer Sciences is one of ten schools at KTH. We conduct research and education in Electrical Engineering, Systems Engineering, Information Technology, Space, Fusion and Plasma Physics. The field of electrical and electronic engineering at KTH is internationally recognized and places high in international rankings. The School is responsible for bachelor and master programs in Electrical Engineering which are closely linked to our research fields. 450 people work together in an international environment in eight departments and in the Dean’s office.

The Department of Network and Systems Engineering consists of about 40 faculty members, researchers and Ph.D. students who contribute to a high professional standard of intensive work and quality results, as well as to a friendly and open environment. The staff has a multicultural background and the working language is English. We are an internationally recognized department investigating most aspects of Networks, Computer and Wireless Networking, Internet of Things, Security and Privacy, and Management of Projects, Systems and Services. Our research spans from theoretical to experimental approaches. We maintain collaborations worldwide with leading universities and major industrial partners.

Project description

We are seeking 1-2 PhD students with a strong background and interest in Mathematics, Optimization, Machine Larning, and Networks, as well as in Experimental and Systems Research. The following research directions will be investigated:

ML and Decision making algorithms for distributed data sets: in large scale systems data sets and nodes are distributed, with data sets whose dimensionality may reach billions. The nodes may not be able to share the data due to the huge data volumes, preventing central collection of data in a timely manner. The distributed nature of the system, especially in IoT and future wireless networks, requires scalable learning methods that adapt gracefully to various network sizes, also when the nodes have limited computational resources. We will establish fundamentally new learning and decision making algorithms for distributed data sets.
ML and Decision-making algorithms over networks: Existing distributed machine learning methods assume perfect coordination of the computations. This is in contrast to the huge data set dimensionality, finite bandwidth of the channels, unreliable links, and delays caused by communication protocols or their composition over heterogeneous networks in IoT and future wireless networks. Moreover, communication can be expensive due to limited energy budget at the nodes. We will investigate fundamentally new algorithms for IoT and future wireless networks.

Further details:
Doctoral student positions in Machine Learning over Networks at KTH Royal Institute of Technology