PhD Candidate in large-scale distributed machine learning
|Workplace||Stockholm, Södermanland, Sweden|
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School of Electrical Engineering and Computer Science at KTHKTH Royal Institute of Technology in Stockholm has grown to become one of Europe’s leading technical and engineering universities, as well as a key centre 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 architecture, industrial management, urban planning, history and philosophy.
Project descriptionThird-cycle subject: Electrical Engineering
With the development of computing and communication technologies, and emerging data-driven applications, e.g., IoT based intelligent systems, social network analysis and vehicular networks, the volume of data for various learning systems increases explosively along with the number of involving computing nodes. Thus, large-scale distributed machine learning (e.g., federated learning, and alternating direction method of multipliers (ADMM)) has been pervasive in our societies and industries. However, the practical performance of DML is often limited by various bottlenecks and is still far from theoretical upper limits. Thus, this project will develop efficient algorithms and schemes to achieve reliable, secure and low-complexity large-scale DML. One key approach is to exploit network and channel coding schemes. Theoretical analysis will be performed using learning and communication tools (e.g., graph model, coding and information theory, optimization). The result will also be verified by simulations with true data.
The project will be hosted by Division of Information Science and Engineering, EECS school. You have opportunities to take advanced Ph.D courses to improve your knowledge backgrounds. The project is funded by Swedish Research Council (VR). You will have opportunities to work on cutting-edge information and data science and engineering.
Supervision: Ming Xiao is proposed to supervise the doctoral student. Decisions are made on admission
What we offer
Admission requirementsTo be admitted to postgraduate education (Chapter 7, 39 § Swedish Higher Education Ordinance), the applicant must have basic eligibility in accordance with either of the following:
In addition to the above, there is also a mandatory requirement for English equivalent to English B/6,
SelectionIn order to succeed as a doctoral student at KTH you need to be goal oriented and persevering in your work. During the selection process, candidates will be assessed upon their ability to:
Knowledge in machine learning/data science as well as knowledge in related mathematical analysis (for machine learning) is required. Knowledge in information theory, coding theory is preferable.
After the qualification requirements, great emphasis will be placed on personal competency.
Target degree: Doctoral degree
Information regarding admission and employmentOnly those admitted to postgraduate education may be employed as a doctoral student. The total length of employment may not be longer than what corresponds to full-time doctoral education in four years ’ time. An employed doctoral student can, to a limited extent (maximum 20%), perform certain tasks within their role, e.g. training and administration. A new position as a doctoral student is for a maximum of one year, and then the employment may be renewed for a maximum of two years at a time.
Union representativesYou will find contact information for union representatives on KTH’s website .
Doctoral section (Students’ union on KTH Royal Institute of Technology)You will find contact information for doctoral section on the section’s website .
ApplicationApply for the position and admission through KTH’s recruitment system. It is the applicant’s responsibility to ensure that the application is complete in accordance with the instructions in the advertisement.
Applications must be received at the last closing date at midnight, CET/CEST (Central European Time/Central European Summer Time).
Applications must include the following elements:
Other informationStriving towards gender equality, diversity and equal conditions is both a question of quality for KTH and a given part of our values.
For information about processing of personal data in the recruitment process please read here.
We firmly decline all contact with staffing and recruitment agencies and job ad salespersons.
Disclaimer: In case of discrepancy between the Swedish original and the English translation of the job announcement, the Swedish version takes precedence.
Type of employment
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In your application, please refer to myScience.org and reference JobID 2287413.
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