WASP looking for seven new Ph.D. students
KTH has grown to become one of Europe’s leading technical and engineering universities, as well as a key center 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 in architecture, industrial management, urban planning, history and philosophy. The seven positions will be placed at three schools within KTH's within the project WASP. Apply before the 15 of June.
Wallenberg Autonomous Systems and Software Program (WASP) is Sweden's largest individual engineering research program ever, and provides a platform for academic research and education, fostering interaction with Sweden's leading technology companies. The program addresses research on autonomous systems and software acting in collaboration with humans, adapting to their environment through sensors, information and knowledge, and forming intelligent systems-of-systems. Software is the main enabler in autonomous systems, and is an integrated research theme of the program. WASP's key values are research excellence and industrial relevance.
The graduate school within WASP is dedicated to provide the skills needed to analyze, develop, and contribute to the interdisciplinary area of autonomous systems and software. The curriculum provides the foundations, perspectives, and state-of-the-art knowledge in the different disciplines taught by leading researchers in the field. Through an ambitious program with research visits, partner universities, and visiting lecturers, the graduate school actively supports forming a strong multi-disciplinary and international professional network between PhD-students, researchers and industry. The graduate school provides added value on top of the existing PhD programs at the partner universities, providing unique opportunities for students who are dedicated to achieving international research excellence with industrial relevance.
WASP involves five Swedish universities: Chalmers, KTH Royal Institute of Technology, Linköping University, Lund University, and Umeå University together with numerous Swedish industries. At KTH, three schools are involved: KTH Electrical Engineering, KTH Computer Science and Communication, and KTH Information and Communication Technology. For more information about each of the KTH schools please visit their respective websites.
KTH offers up to seven Ph.D. positions within the following seven research projects:
Security and Privacy of Autonomous Systems in the Home
Supervisor: Sonja Buchegger, School of Computer Science and Communication, KTH
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Short Project description: Smart-home technology and the Internet of Things increasingly incorporate autonomous systems that can make decisions on our behalf - both to increase the utility and to decrease the complexity for the user. This becomes especially important in home environments for independent life which allow people with reduced activity ranges due to age, illness, or disabilities to live in their own homes longer. Yet such autonomous systems raise concerns about security and privacy. Quantitatively, this means more (sensor) data as well as more interconnected devices leading to increased system complexity. Qualitatively, the collected and inferred data is increasingly personal. This project will analyze these concerns, derive requirements, and, based on cryptographic approaches, develop solutions toward balancing functionality, security, privacy, and performance such that the networked systems can be useful and trustworthy. The latter is a prerequisite for the adoption of such autonomous systems; they must be protected from adversaries that, once having forced access, could change the functionality or leak private data.
Reinforcement learning endowed multi-robot planning and control under temporal logic tasks
Supervisor: Dimos Dimarogonas, School of Electrical Engineering, KTH
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Short Project description: We will consider the problem of distributed task and motion planning for multi-robot systems in unknown and dynamic environments. Robotic tasks in this project will be considered to be of a non-standard nature from a classic control theory viewpoint, since they will be defined in the form of temporal logic/language based formulas from formal verification. Such logics, such as Linear Temporal Logic and Signal Temporal Logic (abbr. LTL/STL) impose specifications to robots that can be seen as a combination of Boolean, time/temporal and state-space/spatial constraints such as “in case of detecting an intruder, return to the base station within 5 minutes and switch on alarm”. Based on the limited sensing and communication capabilities of the robots, we plan to develop distributed reinforcement learning tools for appropriate control policy adaptation that will (i) exploit inter-robot communication to collaboratively learn the common workspace model, and furthermore (ii) iteratively adapt and improve the parameterized local policy to optimally accommodate the updated model and the uncertainties due to other robots’ behaviors. The research will blend elements from machine learning, distributed control and formal verification towards a novel approach to flexible multi-robot task planning and control in unknown and dynamic environments.
Learning Dynamical Systems
Supervisor: Håkan Hjalmarsson, School of Electrical Engineering, KTH
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Short Project description: Learning dynamical systems is an area closely related to machine learning, cyber-physical systems as well as real-time big data analytics, and it provides backbone algorithms for digitalization of industry and society. Among others, it is core technology in autonomous systems with applications such as smart buildings, self-driving vehicles, and self-learning robots. In this project we focus on three key themes: Fundamental techniques concerns learning parsimonious models in a statistical and computationally efficient way. Active and on-line learning concerns how to improve data-efficiency by actively controlling the excitation of the system in a sequential manner. Dynamical networked systems addresses issues of relevance to learning of interconnected dynamical systems, a field rapidly increasing in importance thanks to the fast development of 5g communication technology and the Internet-Of-Things paradigm.
Embedded Optimization for Real-Time Machine-Learning
Supervisor: Mikael Johansson, School of Electrical Engineering, KTH
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Short Project description: An increasing number of our daily decisions are guided by computers that sense, infer and act on the data that they observe. Many of these autonomous decision-making tasks are naturally posed as optimization problems. Examples include finding the best parameters (“training”) of a deep neural network, deriving an optimal decision rule for trading of volatile assets, or determining the best action of an autonomous vehicle under dynamic constraints and uncertain observations. Real-time optimization is becoming a critical technology for making better, faster and more informed autonomous decisions. In this project, we will develop advanced optimization algorithms that are able to run in real-time on embedded hardware. We aim at novel classes of optimization algorithms that can deliver optimal or near-optimal solutions with limited memory, processing and energy resources. Particular attention will be given to algorithms that can exploit emerging multi-core/multi-GPU embedded platforms. The project will blend applied mathematics for algorithm development, implementation of these algorithms on emerging hardware architectures, and application of real-time optimization to autonomous decision-making.
Principled Integration of Logic Reasoning and Deep Learning
Supervisor: Hedvig Kjellström, School of Computer Science and Communication, KTH
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Short Project description: Machine Learning methods based on Deep Neural Networks (DNN) have been tremendously successful in the last few years. The success is especially prominent in domains where large volumes of training data can be acquired, such as Computer Vision and Speech Recognition. However, in domains where the goal is to infer complex causal chains, the needed amount of training data grows rapidly. An example is human-robot collaboration, where the robot might want to infer the goal of a sequence of actions performed by a human, in order to plan its own actions.
On the other hand, humans are able to learn complex models from very few examples. We argue that the key difference between human learning and DNN learning is that humans employ logic reasoning, and knowledge about intuitive physics and intuitive psychology in their learning. Such models, which will combine DNN with logic reasoning and probabilistic models in a principled manner, would enable learning of much more complex models from much less training data - a major breakthrough in Deep Learning!
Autonomous Time-Critical Cloud
Supervisor: Dejan Kostic, School of Information and Communication Technology, KTH
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Short Project description: In a number of time-critical societal applications, besides providing high reliability and throughput a very important property the cloud infrastructure has to provide is guaranteed low latency for delivering data. This feature is sorely lacking today, with the so-called tail-latency of slowest responses in popular cloud services being several orders of magnitude longer than the median response times. Unfortunately, simply using a network infrastructure with ample bandwidth does not guarantee low latency because of problems with congestion at the intra-and inter-data center level, sub-optimal routing, server overload, etc. The goal of this PhD position is to develop the necessary technology for time-critical cloud services. Examples include advanced software-defined control of the network, highly streamlined network functions virtualization, and geo-distributed data storage systems. The student will work as a part of the team developing autonomic solutions for time-critical data transmission, which will be evaluated on the test case in intelligent transport systems.
Trustworthy Internet of Things
Supervisor: Panagiotis Papadimitratos, School of Electrical Engineering, KTH
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Short Project description: A wireless revolution has been materializing, transforming our environments and processes into ‘intelligent’ ones through an Internet of Things (IoT) and increasing levels of autonomous operation. The benefits include, for example, ubiquitous medical services, smart energy production and distribution, automated logistics chains, adaptable buildings, more effective tactical operations, more efficient and safer transportation. We have every reason to embrace the IoT and make our lives and businesses easier. But to do so, we need to ensure our systems are secure and privacy preserving. Otherwise, critical processes could come to a halt, individuals could be hurt and their privacy be comprised. This is exactly the motivation for this line of work. We are looking for candidates that can contribute high-quality research towards the design, implementation, evaluation, and analysis of secure and privacy-preserving IoT systems. Skills and background on systems, or formal methods, or information theory are required, along with a solid understanding of technology; combined profiles are a plus. The objective of this work is to contribute to the next wave of security and privacy solutions for the IoT, strengthen the theoretical foundations, instantiate security and privacy for key applications, and catalyze the adoption of resilient, trustworthy IoT systems.
The successful applicant is expected to hold or to be about to receive an MSc degree in Information and Communication Technology, Electrical Engineering, Engineering Physics, Computer Science or equivalent.
The successful applicant should have an outstanding academic track record, and well developed analytical and problem solving skills. We are looking for a strongly motivated person, who is able to work independently. Good command of English orally and in writing is required to publish and present results at international conferences and in international journals.
Trade union representatives
You will find contact information to trade union representatives at KTH:s webpage.
Application deadline is the 15th of June 2017. In order to apply, log into KTH:s recruitment system. You are the main responsible to ensure that your application is complete according to the ad. Your complete application must be received at KTH no later than the last day of application, midnight CET/CEST (Central European Time/Central European Summer Time).
You may apply for at most two of the research projects. If you apply for two projects, you need to mention in your application which project is your first choice.
The application must include:
- CV including your relevant professional experience and knowledge.
- Copy of the degree certificate(s) and transcripts of records from your previously attended university-level institutions. Translations into English or Swedish if the original documents are not issued in one of these languages.
- Statement of purpose: Why do you want to pursue a PhD, what are your academic interests, how they relate to your previous studies and future goals; maximum 2 pages long.
- Representative publications or technical reports: Documents no longer than 10 pages each. For longer documents (e.g. theses), please provide a summary (abstract) and a web link to the full text.
- Letters of recommendation
- Contact information for two reference persons. We will contact reference persons only for top-listed candidates
Application deadline: 2017-06-15
The employment is time limited following the regulations for Ph.D. employment in the Higher Education Ordinance.
Type of employment: Temporary position longer than 6 months
Working hours: Full time
Access: According to agreement, preferably as soon as possible
Salary: Monthly salary according to KTH´s Ph.D. student salary agreement
Number of positions: 7
Bo Wahlberg, KTH WASP Director:
Sofia Wiklund, HR officer: