WASP announces call for postdoc positions in expedition projects
within AI, autonomous systems and software.
The expedition concept is modelled on NSF’s Expeditions in Computing and can be described as high gain/high risk targeted expeditions with a specific challenging goal rather than long term thematic frameworks. An expedition consists of two PIs and two to four PostDocs during two years. Applicants will be part of teams across departments or universities, with the aim to address compelling research topics that promise disruptive innovations in AI, autonomous systems and software for several years to come.
The expedition project positions that are offered in this call are listed below. Please follow the link to the coordinating university to read more and to apply.
Note the different last days for application.
ICARUS—Intelligent Cell-free Access for wiReless Ubiquitous Services
In this project, we will lay the practical foundation for operating autonomous “cell-free” wireless networks. Instead of breaking down the network operation into independent cells, all the access points in a cell-free network serve all users. The price to pay is a more challenging network operation. In this project, we will consider deep learning as a tool to overcome these challenges. We will apply existing methods and also develop new algorithms for the training problem with the aim to efficiently and reliably train deeper neural networks.
For this project four PostDoc positions are offered, two at Linköping University and two at Lund University. The contact persons are Emil Björnson, Division of Communication Systems, Department of Electrical Engineering, Linköping University, firstname.lastname@example.org, Announcement link to be provided, and Pontus Giselsson, Department of Automatic Control, Lund University, email@example.com, Position announcement (deadline January 2, 2019).
High-Confidence Formal Verification of Real Cyber-Physical Systems: from Models to Machine Code
For this project three PostDoc positions are offered, two at KTH and one at Chalmers. Contact persons are David Broman, KTH, firstname.lastname@example.org and Magnus Myreen, Chalmers, email@example.com . Announcement links to be provided.
CorSA: Correct-by-design and Socially Acceptable Autonomy
As autonomous systems move from constrained environments into the social world in the form of self-driving vehicles and service robots, their interaction with and around people becomes inevitable. As a consequence, not only guarantees on their safety and performance are crucial, but social acceptability also becomes a key requirement. This project proposes to move from conventional correct-by-design control with simple, static human models towards the synthesis of correct-by-design and socially acceptable controllers that consider complex human models based on empirical data.
Our research objectives are:
(1) development of data-driven models of human behavior that enable the autonomous system to better respond in real-world situations and contribute to the social acceptability of the system; and (2) design of formal methods-based human-in-the-loop assumption-guarantee decision making and control synthesis algorithms, where the measure of social acceptability comes as an optimization criterion.
For this project two PostDoc positions are offered at KTH. The contact persons are Iolanda Leite, KTH, firstname.lastname@example.org Position announcement, and Jana Tumova, KTH, email@example.com Position announcement (deadline for both positions January 15, 2019)
DEBLOAT: Code debloating from source code to large-scale deployment
The DEBLOAT WASP expedition focuses on the design of algorithms and the development of tools that remove unused code from the source code of the application down to the low-level systems libraries. This Expedition is motivated by the growing bloat in software applications : they integrate massive quantities of code that is never used in production but considerably reduces the speed of software wastes resources, and increases risks of failure, and vulnerabilities.
For this project two PostDoc positions are offered, one at KTH and one at Umeå University. The contact persons are Benoit Baudry, Department of Software and Computer Systems, KTH, firstname.lastname@example.org , Position announcement (deadline January 15, 2019), and Erik Elmroth, Department of Computing Science, Umeå University, email@example.com , Position announcement (deadline January 31, 2019)
Realtime Individualization of Brain Computer Interfaces
Controlling the physical world with our mind only, opens up for a vast number of exciting opportunities. Over the recent years, brain computer interfaces (BCIs) have improved steadily, both from advances in algorithms and in hardware. BCIs based on high time resolution electroencephalography (EEG) have a great potential in a wide area of applications. The overarching idea is to apply a tight feedback loop around the complete individualized BCI learning process. By connecting all stages of the learning process, including feature extraction from the input brain data to the classification of neural patterns, the aim is to drastically decrease the problem with long learning times and improve accuracy and reliability. Utilizing and extending a number of state-of-the-art techniques, such as optimal time-frequency representations, Riemann geometry based learning, dual control theory and cognitive modeling combined with edge based cloud computing the intention is to provide an efficient platform for next-generation BCIs, demonstrable within the time span of the project as a consumer-friendly EEG BCI.
Statistical signal processing subject description:
The research project is directed to signal representation, especially using time-frequency techniques and classification methods. The objective is development of robust spectral analysis methods and collaborative research including machine learning techniques and practical experiments with brain computer interfaces.
Control subject description:
The research project is multidisciplinary combining statistics, feedback control and machine learning techniques with data from practical experiments on brain computer interfaces.
For this project two PostDoc positions are offered, both at Lund University. The contact persons are Maria Sandsten, Mathematical Statistics, Centre for Mathematical Sciences, Lund University, firstname.lastname@example.org, Position announcement (deadline January 15) and Bo Bernhardsson, Department of Automatic Control, Lund University, email@example.com, Position announcement (deadline January 15).
Massive, Secure, and Low-Latency Connectivity for IoT Applications
This expedition project aims at designing secure, privacy-preserving, and low-latency wireless connectivity solutions for cloud computing. We will target a multi-client scenario where a large number of sporadically active, resource-limited devices communicate to a powerful but untrusted cloud server. The goal is to establish a foundation for security and privacy in resource- and delay-constrained, multi-client and possibly multi-server cloud-assisted computing. We will characterize from a fundamental perspective how latency and energy efficiency scale as a function of the number of devices connected to the cloud server, as well as how these two quantities depend on the level of security and privacy provided by the communication and cloud-computing protocol.
The analysis will rely on tools from nonasymptotic information theory, statistics, signal processing, cryptography, and differential privacy.
For this project two PostDoc positions are offered, both at Chalmers. The contact persons are Giuseppe Durisi, firstname.lastname@example.org, Department of Electrical Engineering, Position announcement (deadline December 20) and Katerina Mitrokotsa email@example.com, Department of Computer Science and Engineering, Position announcement (deadline January 5).
The research in this project will focus on development of autonomous optimization, i.e. how to use tools from machine learning to automatically design and tune optimization algorithms. In particular, we plan to investigate how to learn new efficient distributed optimization methods suitable for decision-making in autonomous systems and AI/ML. The main research challenges of the project are in the intersection between optimization, control and machine learning. This is a collaborative project between the Optimization Group headed by Professor Anders Hansson within the Division of Automatic Control at the Department of Electrical Engineering at Linköping University and the Department of Automatic Control headed by Professor Bo Wahlberg at KTH Royal Institute of Technology, Stockholm, Sweden.
For this project two PostDoc positions are offered, one at KTH and one at Linköping University. The contact persons are Anders Hansson, Division of Automatic Control, Department of Electrical Engineering, Linköping University, firstname.lastname@example.org , Position announcement, and Bo Wahlberg, Department of Automatic Control, email@example.com, Position announcement (deadline for both positions January 31, 2019)