20/06/2020

Postdoc in robust machine learning

This job offer has expired


  • ORGANISATION/COMPANY
    Umea University
  • RESEARCH FIELD
    Computer scienceComputer systems
  • RESEARCHER PROFILE
    First Stage Researcher (R1)
  • APPLICATION DEADLINE
    10/08/2020 00:00 - Europe/Brussels
  • LOCATION
    Sweden › Umea
  • TYPE OF CONTRACT
    Temporary
  • JOB STATUS
    Full-time
  • HOURS PER WEEK
    40
  • OFFER STARTING DATE
    01/09/2020

Umeå University, Department of computing science

Umeå University is dedicated to providing creative environments for learning and work. We offer a wide variety of courses and programmes, world leading research, and excellent innovation and collaboration opportunities. More than 4 100 employees and 34 000 students have already chosen Umeå University. We welcome your application!

Umeå University, the Department of Computing Science, is seeking outstanding candidates for a postdoc position in robust machine learning and data-centric optimization with focus on scarce data and non-standard model settings. Deadline for application is August 10, 2020.

Project description

We invite candidates to apply for one postdoc 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 rapid increase of autonomous systems and applications are providing challenges in dealing with both scarce and petabytes of data in diverse environments. Machine learning has recently achieved a lot with the standard assumption that availability of data is large. However, it still remains many challenges in areas where this is not true. These sizes and heterogenous features make the machine learning models larger and more complex. Classical approaches to training, learning, and inference fail to address the problems of scarce data, non-standard model settings, data-centric optimization (centralized and distributed), communication, computation, synchronization, and many more. This project focuses on design and implementation of robust machine learning and data-centric optimization algorithms for scarce and petabytes of data and non-standard model settings, which are ideally suited for constraint environments and edge infrastructures.

This project aims to design and implement robust learning and data-centric optimization techniques for advancing state-of-the-art machine learning algorithms where data is geographically distributed, sensitive, and scarce. Robust machine learning and data-centric optimization algorithms empower models through multi-level (local, global and hybrid) training, learning, and inference with data-centric optimization for scarce data and non-standard model settings. By creating unique features (e.g., decentralized training, learning and inference, fault-tolerant against failures and attacks, data-centric optimization, robustness), this project addresses the challenges in the following areas: robust learning; learning with scarce data and non-standard model settings; lack of theoretical knowledge to build manual models; computation efficient learning and optimization for obtaining more accurate and robust models with applications to constraint environments (i.e., Industrial Internet of Things (IIoT), healthcare systems) and edge infrastructures.

The position is at the Department of Computing Science at Umeå University, Sweden. The selected candidate is expected to contribute towards the local research community by actively participating in the departmental and group research activities such as workshops, seminars, etc. These contributions can be within autonomous distributed systems lab, but collaboration with researchers in, e.g., machine learning, mathematical statistics, optimization, trustworthy learning and artificial intelligence is expected. (For further information, see www.cloudresearch.org).

Qualifications

Applicants must have earned a PhD or a foreign degree that is deemed equivalent to a PhD in Artificial Intelligence, Machine Learning or Optimization for Machine Learning, Computer Science or a subject relevant for the position. The PhD degree should not be more than three years old by the application deadline, unless special circumstances exist.

Candidates are expected to have outstanding knowledge of machine learning and optimization techniques. Demonstrable knowledge of data wrangling and learning in decentralized settings is a prerequisite. Experience in any of the areas robust learning, fault-tolerant learning and data-centric optimization when data is geographically distributed, sensitive and scarce is a merit.

Since research is conducted in an international research environment, the ability to collaborate and contribute to teamwork, and an excellent command of the English language, both written and spoken, are essential requirements.

We particularly invite female candidates to apply to ensure gender balance.

Terms of employment

The appointment is for two years full-time employment. Postdocs are typically offered the opportunity to gain teaching experience on suitable undergraduate courses. Expected starting date is October 1 or as otherwise agreed.

Submitting your application

A complete application should contain the following documents:

  • Introductory letter including a 2-page statement of research interests relative to the above topics and a motivation of why your expertise is appropriate for the position.
  • Curriculum Vitae (CV) including a complete list of scientific publications.
  • Copies of degree certificates, including documentation of completed academic courses and obtained grades
  • A copy of your PhD thesis and copies of (max 5) original research publications relevant to the above topics, numbered according to the publication list.
  • Names and contact information for three persons willing to act as references.
  • Any other information relevant for the position such as description of software development experience, or previous industry experience.

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 August 10, 2020.

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 130 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, clouds and distributed systems.

We look forward to receiving your application!

Umeå University wants to offer an equal environment where open dialogue between people with different backgrounds and perspectives lay the foundation for learning, creativity and development. We welcome people with different backgrounds and experiences to apply for the current employment.

We kindly decline offers of recruitment and advertising help.

Type of employment
Temporary position longer than 6 months

Contract type
Full time

First day of employment
September 1, 2020 or as otherwise agreed

Salary
Monthly

Number of positions
1

Working hours
100

City
Umeå

County
Västerbottens län

Country
Sweden

Reference number
AN 2.2.1-938-20

Contact

  • Monowar Bhuyan, 090-786 67 05
  • Erik Elmroth, 090-786 69 86

Union representative

  • SACO, 090-786 53 65
  • SEKO, 090-786 52 96
  • ST, 090-786 54 31

Published
17.Jun.2020

Last application date
10.Aug.2020 11:59 PM CET

Benefits

Terms of employment

The appointment is for two years full-time employment. Postdocs are typically offered the opportunity to gain teaching experience on suitable undergraduate courses. Expected starting date is October 1 or as otherwise agreed.

Submitting your application

A complete application should contain the following documents:

  • Introductory letter including a 2-page statement of research interests relative to the above topics and a motivation of why your expertise is appropriate for the position.
  • Curriculum Vitae (CV) including a complete list of scientific publications.
  • Copies of degree certificates, including documentation of completed academic courses and obtained grades
  • A copy of your PhD thesis and copies of (max 5) original research publications relevant to the above topics, numbered according to the publication list.
  • Names and contact information for three persons willing to act as references.
  • Any other information relevant for the position such as description of software development experience, or previous industry experience.

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 August 10, 2020.

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

Offer Requirements

  • REQUIRED EDUCATION LEVEL
    Computer science: PhD or equivalent
  • REQUIRED LANGUAGES
    ENGLISH: Excellent

Skills/Qualifications

Applicants must have earned a PhD or a foreign degree that is deemed equivalent to a PhD in Artificial Intelligence, Machine Learning or Optimization for Machine Learning, Computer Science or a subject relevant for the position. The PhD degree should not be more than three years old by the application deadline, unless special circumstances exist.

Since research is conducted in an international research environment, the ability to collaborate and contribute to teamwork, and an excellent command of the English language, both written and spoken, are essential requirements.

We particularly invite female candidates to apply to ensure gender balance.

Specific Requirements

Candidates are expected to have outstanding knowledge of machine learning and optimization techniques. Demonstrable knowledge of data wrangling and learning in decentralized settings is a prerequisite. Experience in any of the areas robust learning, fault-tolerant learning and data-centric optimization when data is geographically distributed, sensitive and scarce is a merit.

Map Information

Job Work Location Personal Assistance locations
Work location(s)
1 position(s) available at
Umea University
Sweden
Vasterbotten
Umea
90187
Department of Computing Science

EURAXESS offer ID: 534691

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