Umeå University, Department of Computing Science

Umeå University is one of Sweden’s largest higher education institutions with over 37,000 students and about 4,700 employees. The University offers a diversity of high-quality education and world-leading research in several fields. Notably, the groundbreaking discovery of the CRISPR-Cas9 gene-editing tool, which was awarded the Nobel Prize in Chemistry, was made here. At Umeå University, everything is close. Our cohesive campuses make it easy to meet, work together and exchange knowledge, which promotes a dynamic and open culture.

The ongoing societal transformation and large green investments in northern Sweden create enormous opportunities and complex challenges. For Umeå University, conducting research about – and in the middle of – a society in transition is key. We also take pride in delivering education to enable regions to expand quickly and sustainably. In fact, the future is made here.

Are you interested in learning more? Read about Umeå university as a workplace

Umeå University, the Department of Computing Science, is seeking outstanding candidates for a PhD student position in Computer Science with focus on distributed deep learning anomaly detection for distributed clouds and Internet of Things (IoT). Deadline for application is February 20, 2020.

The position is 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

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
The main goal is to develop methods, primarily distributed deep learning algorithms, for anomaly detection, prediction and diagnosis in distributed clouds and IoT by taking advantages of multilayer cloud architecture from edge to datacenter. In the era of big data, speedy and accurate learning from a large volume of heterogeneous data (i.e., video, voice, image, sensor data) using centralized models boost challenges for automation, computation and performance efficient detection. Distributed deep learning will address these issues through developing methods that combine local (the client) and global (the centralized controller) learning and inference for anomaly detection. Initial decisions are taken on-device and final decisions at the centralized controller after updating of data. The use of layered compute nodes (i.e., edge or fog or distant datacenter) will increase the reliability and robustness of the learning models.

The anomaly detection, prediction and diagnosis are vital for ensuring the performance and security of distributed clouds and IoT. These problems still open to address primarily focuses on increasing reliability and robustness of learning algorithms when decentralizing computation from edge to distant clouds. We propose to develop distributed deep learning approaches for security and performance anomaly detection in distributed clouds and IoT.

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

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 second-cycle level degree equivalent to 60 ECTS credits in computing science, or in another subject considered to be directly relevant for the specialization in question.

Documented knowledge and a solid background in machine learning and/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, 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 cover letter including a description of your research interests, your reasons to apply for the position, and your contact information
  • - A curriculum vitae
  • - Copies of degree certificates and other completed academic courses
  • - Reprints / copies of Bachelors / Masters thesis, and other relevant publications, if any
  • - Contact information for three reference persons
  • - 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. Application must be submitted electronically using the e-recruitment system of Umeå University, and be received no later than February 20, 2020

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: and Professor Erik Elmroth (email:

More about us:
The Department of Computing Science is a dynamic environment with over 120 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 Distributed Systems group is to design, develop, deploy distributed learning algorithms for (autonomous) resource and application management for different types of IoT, clouds and distributed systems.

We look forward to receiving your application!

Type of employment Temporary position
Contract type Full time
First day of employment As soon as possible, or as otherwise agreed
Salary Monthly salary
Number of positions 1
Full-time equivalent 100%
City Umeå
County Västerbottens län
Country Sweden
Reference number AN 2.2.1-1886-19
  • Monowar Bhuyan, biträdande lektor,
Union representative
  • SACO, 090-786 53 65
  • SEKO, 090-786 52 96
  • ST, 090-786 54 31
Published 04.Dec.2019
Last application date 20.Feb.2020 11:59 PM CET

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