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Doctoral student in deep learning for turbulence

8 days ago | Closing on Aug 01
KTH Royal Institute of Technology
Doctoral student in deep learning for turbulence
KTH Royal Institute of Technology
KTH in Stockholm is the largest and oldest technical university in Sweden. No less than one-third of Sweden’s technical research and engineering education capacity at university level is provided by KTH.
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JOB DETAILS
Published: 8 days ago
Application deadline: Aug 01
Location: Stockholm, Sweden
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Doctoral student in deep learning for turbulence

KTH 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 description

Third-cycle subject: Engineering Machanics, Fluid Mechanics

The idea of the present project is to apply deep learning techniques to predict the flow in various situations, using different types of datasets as training data. The availability of large labelled datasets, along with high-end computational units (e.g. GPUs) and public software packages have made it possible for improvements in performance of learning systems in various domains including computer vision and speech recognition. These advances are mostly in supervised scenarios where a deep network is able to learn a rich representation of the data, through various layers of nonlinear transformations, which makes the approximation of highly complex functions simpler. Within this project, we aim to bring the recent algorithms developed in the area of machine learning and computer vision to model the flow in controlled (simulated) scenarios. In particular, a fully-connected network with various numbers of layers and units per layer will be tried as a baseline. Machine learning techniques for general applications, in particular fluid mechanics, will be developed. The ultimate goal is to reproduce datasets from direct numerical simulations (DNSs) of wall-bounded turbulent flows, but we will start by assessing the performance of the deep learning approach with simplified turbulent flow data.

What we offer

  • The possibility to study in a dynamic international research environment in close cooperation with industries and advanced universities the world over.
  • Life-long network through KTH´s Alumni organization.
  • A personal study plan to support your development within your areas of interest.
  • Monthly salary according to KTH´s Ph.D. student salary agreement. KTH´s employee benefits.
  • Work and study in Stockholm, one of Europe's fastest growing capitals, which is close to both nature and the sea.
  • Help to relocate and get settled in Sweden and at KTH
  • The project will be part of SeRC (Swedish e-Science Research Centre) and the Linné FLOW Centre, and as such profit from the wide network of experts in e-Science and fluid mechanics at KTH. The project is also in collaboration with the RPL (Robotics, Perception and Learning) department, and the student will be part of the research meetings in this group.

Eligibility

To be admitted to postgraduate education (Chapter 7, 39 § högskoleförordningen), the applicant must have basic eligibility in accordance with either of the following:

  • passed a degree at advanced level,
  • completed course requirements of at least 240 higher education credits, of which at least 60 higher education credits at advanced level, or
  • in any other way acquired within or outside the country acquired essentially equivalent knowledge.

A suitable background for this position would be a Master of Science in Computer Science, Physics, Mathematics or Mechanics with a specialization in fluid mechanics, computational methods and/or applied mathematics. In addition to the traditional academic merits, a relevant degree project, international experience, and language skills are regarded as advantageous qualifications. 

Selection

In order to succeed as an doctoral student at KTH you need to be goal oriented and persevering in your work. In the selection of the applicants, the following will be assessed:

  • ability to independently pursue his or hers work,
  • ability to collaborate with others,
  • have a professional approach and
  • analyse and work with complex issues.

Applicants must be strongly motivated for doctoral studies, possess the ability to work independently and perform critical analysis as well as possessing good levels of cooperative and communicative abilities.

After the qualification requirements, great emphasis will be placed on personal qualities and personal suitability.

Target degree: Doctoral

Information regarding admission and employment

Only those who are or have been admitted to third-cycle studies may be employed as a doctoral student. The term of the initial contract may not exceed one year. The employment may be extended for a maximum of two years at a time. However, the total period of employment may not exceed the equivalent of four years of full-time study. Decisions on employment as a doctoral student may not be appealed.

Eligibility, selection and admissions are regulated in antagningsordning till utbildning vid Kungl. Tekniska högskolan and the program description for the doctoral program and current subject syllabus available on KTH's website.

Doctoral students should primarily devote themselves to their own education, but may engage in teaching, research, and administration corresponding to a maximum of 20 % of a full-time position.

Union representatives

You 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.

Application

Apply 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:

  • Applications must include the following elements:
  • CV including your relevant professional experience and knowledge.
  • Statement of purpose: Why do you want to pursue a PhD, what are your academic interests, how they relate to your previous studies and future goals; maximum 2 pages long.
  • Copy of the degree certificate(s) and transcripts of records from your previously attended university-level institutions. Translations into English or Swedish if the original documents are not issued in one of these languages.
  • Representative publications or technical reports: Documents no longer than 10 pages each. For longer documents (e.g. theses), please provide a summary (abstract) and a web link to the full text.

Other

We firmly decline all contact with staffing and recruitment agencies and job ad salespersons.

Gender equality, diversity and zero tolerance against discrimination and harassment are important aspects of KTH´s work with quality as well as core values in our organization.

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: Temporary position longer than 6 months
Contract type: Full time
First day of employment: Autumn 2018 according to agreement
Salary: Monthly salary according to KTH´s Ph.D. student salary agreement
Number of positions: 1
Working hours: 100%
City: Stockholm
County: Stockholms län
Country: Sweden
Reference number: S-2018-0868
Contact:
  1. Assistant Professor Ricardo Vinuesa, e-mail: rvinuesa@mech.kth.se
  2. Assistant Professor Hossein Azizpour, e-mail azizpour@kth.se,
  3. Associate Professor Philipp Schlatter, e-mail: Pschaltt@mech.kth.se
Published: 2018-06-14
Last application date: 2018-08-01 Continue reading
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