Chalmers University of Technology, Sweden - Logo

Data Science, PhD Vacancy in Sweden – Computational Complexity of Statistical Inference

Chalmers University of Technology (CUT)

Sweden

Chalmers University invites PhD student position in the Computational Complexity of Statistical Inference at the Department of Computer Science and Engineering, WASP Graduate School – Sweden, Oct 2021

Support us by Sharing!

ADVERTISEMENT
Scroll Down for Content

General Info

Position: PhD
No. of Positions: 1
Research Field: ,
Deadline to Apply: Expired
Joining Date: -
Contract Period: Not Mentioned
Salary: According to Standard Norms

Workplace:
Wallenberg AI, Autonomous Systems and Software Program)
Department of Computer Science and Engineering
Chalmers University of Technology (CUT)
Gothenburg,

ADVERTISEMENT
Scroll Down for Content

Qualification Details

To qualify as a PhD student, you must have a master's level degree corresponding to at least 240 higher education credits in a relevant field or expect to complete that degree by the time the employment starts.

The position requires sound verbal and written communication skills in English. If Swedish is not your native language, Chalmers offers Swedish courses. Both Swedish and English are used in undergraduate courses.

Responsibilities/Job Description

Your main responsibility as a PhD student is to pursue your doctoral studies within the framework of the outlined research project. You will be enrolled in a graduate program in the Department of Computer Science and Engineering. You will also be part of the WASP Graduate School and will be able to take the courses and participate in other scientific activities provided by WASP. You are expected to develop your own ideas and communicate scientific results orally as well as in written form. In addition, the position includes 20% departmental work, mostly as a teaching assistant in Chalmers' undergraduate and masters-level courses or performing other departmental tasks.

How to Apply?

Online Application through "Apply Now" Button from this page or from advertisement webpage (URL below)

Reference Number: -20210423
(If any, use it in the necessary place)

ADVERTISEMENT
Scroll Down for Content

Documents Required

The application should be marked with Ref 20210423 and written in English. The application should be sent electronically and be attached as pdf-files, as below:

CV: (Please name the document: CV, Family name, Ref. number)• CV• Other, for example previous employments or leadership qualifications and positions of trust.• Two references that we can contact.

Personal letter: (Please name the document as: Personal letter, Family name, Ref. number)1-3 pages where you:• Introduce yourself• Describe your previous experience of relevance for the position (e.g. education, thesis work and, if applicable, any other research activities)• Describe your future goals and future research focus

Other documents:• Copies of bachelor and/or master’s thesis.• Attested copies and transcripts of completed education, grades and other certificates, e.g. TOEFL test results.Please use the button at the foot of the page to reach the application form. The files may be compressed (zipped).

About the Position

The PhD project will be focused on the computational complexity of statistical inference. Classical methods in statistics often focus on identifying limits on statistical estimation in terms of the amount of data or the signal-to-noise ratio required for estimation to be possible. However, in many cases where statistical estimation is known to be possible (e.g. detecting communities in a social network, the sparse version of principal component analysis etc.) all known algorithms for estimation require computationally inefficient brute force search. At the same time the best computationally efficient algorithms only work at much lower signal-to-noise ratios, or by consuming much larger amounts of data than the known inefficient algorithms. This phenomenon suggests that there are fundamental computational limits for statistical inference, and that there is a need to develop a mathematical theory, which goes beyond classical statistics, to explain these limits. In this project, we will contribute to the development of such a theory by utilizing mathematical tools from the theory of approximation algorithms and computational complexity theory. We will also explore connections between the complexity of statistical inference and classical questions in algorithms and complexity theory.The PhD position is supported by WASP (Wallenberg AI, Autonomous Systems and Software Program), a major national initiative and the largest individual research program in Sweden.

Information about the division

The Department of Computer Science and Engineering is a joint department at Chalmers University of Technology and the University of Gothenburg, with activities on two campuses in the city of Gothenburg. The department has around 270 employees from over 30 countries. Our research has a wide span, from theoretical foundations to applied systems development. We provide high quality education at Bachelor's, Master's and graduate levels, offering over 120 courses each year. We also have extensive national and international collaborations with academia, industry and society.

About the Employer: Chalmers University of Technology (CUT)

ADVERTISEMENT
Scroll Down for Content

Contact details

For questions, please contact: Assistant Professor Jonah Brown-CohenE-mail:jonahb@chalmers.se

Advertisement Details: PhD student position in the Computational Complexity of Statistical Inference

Other Vacancies from this field ,

ADVERTISEMENT
Scroll Down for Content

Support us by Sharing!