PhD student position in Computer Science with focus on trustworthy anomaly detection - Befattningen är under tillsättning eller redan tillsatt, och det är inte längre möjligt att lämna in en ansökan

Umeå University, the Department of Computing Science, is seeking outstanding candidates for a PhD student position in Computer Science with focus on trustworthy learning for anomaly detection in complex data. Deadline for application is 22 August, 2021.

The position funded by The Knut and Alice Wallenberg Foundation through The Wallenberg AI, Autonomous Systems and Software Program (WASP), Sweden’s largest ever individual research program, and a major national initiative for strategic basic research, education and faculty recruitment. The vision of WASP is excellent research and competence in artificial intelligence, autonomous systems and software for the benefit of Swedish society as well as industry. For more information about the research and other activities conducted within WASP please visit http://wasp-sweden.org/.

The graduate school within WASP provides 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. It thus 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.

Project description

Classical machine learning algorithms are more trustworthy than deep learning because they are less complex and less opaque. On the other hand, they have large disadvantages. The use of machine learning for defense of computer systems against growing security and privacy attacks accelerate challenges to ensure accurate, reliable and robust models. Because security and privacy of learning models are largely ignored, though they are key for safety and security critical application domains such as healthcare, automotive and robotics, industry 4.0, and cyber-physical systems. Such attacks can manipulate, evade, fool, misled the learning models or systems at any levels, e.g., data, model, and inference. As a result, current detection and defense models lead to catastrophic performance, loose user’s privacy and trust, and also incurred a substantial financial loss for cloud service providers. Hence, the proposed models for detection, defense, and root-cause analysis of anomalies need to be more robust and resilient to both security and privacy attacks.

The aim of this project is primarily to develop trustworthy learning methods for anomaly detection, defense, and root-cause analysis in complex data (e.g., heterogenous, multi-source, data with extreme missing values) to increase model robustness, adaptability, resilience, and transparency. We propose to design and implement trustworthy machine learning algorithms for anomaly detection, defense and root-cause analysis under adversarial settings. These algorithms rigorously investigate the input, model and output leveraging (a) geometric and statistical distribution of data, (b) adversarial features with significant amount of attack variation, (c) internal behavior analysis of models, (d) model-agnostic vulnerability analysis, (e) security and privacy-aware design of models to address the evolving adversarial attacks. These features improve the performance, scalability, robustness and transparency of the data, models and inference. They will also have great potential for application to edge clouds, Internet of Things (IoT), healthcare, and industry 4.0 under adverse conditions.

The position is aimed for graduate studies in Computing Science within the autonomous distributed systems lab, but collaboration with researchers in, e.g., machine learning, mathematical statistics, optimization, trustworthy learning, deep learning or artificial intelligence is expected. (For further information, see www.cloudresearch.org).

Admission requirements

The applicant is required to have completed a second-cycle level degree, or completed course requirements of at least 240 ECTS credits, of which at least 60 ECTS credits are at second-cycle level, or have an equivalent education from abroad, or equivalent qualifications.

To fulfill the specific entry requirements for doctoral studies in computing science the applicant is required to have completed courses at advanced level in computing science or another subject considered to be directly relevant for the specialization in question, equivalent to 60 ECTS credits.

Documented knowledge and a solid background in machine learning and security or distributed systems is a requirement. The research is to a large extent interdisciplinary, and a broad competence profile and experience from other relevant areas (such as machine learning, distributed learning, adversarial learning, deep learning, IoT, discrete optimization, and statistical methods) is considered a merit.

Important personal qualities are, beside creativity and a curious mind, the ability to work both independently and in a group and experience in the scientific interaction with researchers from other disciplines and in other countries. Good skills in both spoken and written English are a requirement for the position. 

Terms of employment

The position is aimed for PhD studies and research during four years, leading to a PhD degree. It is mainly devoted to postgraduate studies (at least 80% of the time), including to take part in the WASP Graduate School, but may include up to 20% department service (usually teaching). If so, the total time for the position is extended accordingly (up to maximum five years). The employment will start as soon as possible, or as otherwise agreed.

Submitting your application

A complete application should contain the following documents:

a) A cover letter including a description of your research interests, your reasons to apply for the position, and your contact information

b) A curriculum vitae

c) Copies of degree certificates and other completed academic courses

d) Reprints / copies of Bachelor’s / Master’s thesis, and other relevant publications, if any

e) Contact information for three reference persons

f) Documentation and description of other relevant experiences or competences, such as from software development and work in or with industry.

The application must be written in English or Swedish. Documents must be in Word or pdf format. Applications must be submitted electronically using the e-recruitment system of Umeå University, and be received no later than 22 August, 2021. 

The department of Computing Science is actively striving for gender balance, and thus encourages applications from women.

The procedure for recruitment for the position is in accordance with the Higher Education Ordinance (chapt. 12, 2 §) and the decision regarding the position cannot be appealed.

Further information can be obtained from Assistant Professor Monowar Bhuyan, (email: monowar@cs.umu.se) and Professor Erik Elmroth (email: elmroth@cs.umu.se).

More about us:

The Department of Computing Science is a dynamic environment with over 140 employees representing more than twenty countries worldwide. We conduct education and research on a broad range of topics in Computing Science. The focus of the research in the Autonomous Distributed Systems Lab is to design, develop, deploy distributed learning algorithms for (autonomous) resource and application management for different types of IoT, IIoT, edge clouds and large-scale complex systems.

We look forward to receiving your application!