WASP AI Program PhD Position in Mathematical Statistics

The Department of Mathematics and Mathematical Statistics at Umeå University is opening a PhD position in mathematical statistics, focused on statistical learning with sparsity and deep network models. The position is for four years of research, including courses at the graduate level. Last day to apply is August 30, 2019.

The expansion of Artificial Intelligence (AI), in the broad sense, is one of the most exciting developments of the 21st century. This progress opens up many possibilities but also poses grand challenges. The centre Wallenberg AI, Autonomous Systems, and Software Program (WASP) is launching a program to develop the mathematical side of this area. The aim is to strengthen the competence of Sweden as a nation within the area of AI and we are taking part of this program through this specific project. The vision of WASP is excellent research and competence in artificial intelligence, autonomous systems and software for the benefit of Swedish industry. For more information about the research and other activities conducted within WASP please visit http://wasp-sweden.org/

Project description and tasks
Industrial robots, autonomous cars, stocks trading algorithms, and deep network assisted evaluation of medical images all crucially involve real-time, intelligent and automated decision making from complex and heterogeneous data, at ever growing scale and pace. This presents unprecedented theoretical and algorithmic challenges and opportunities for researchers in intelligently collecting and transforming this data into information, predictions and intelligent decisions.

Modern statistical learning is at the forefront of these advances, and the main objective of this doctoral position is to develop cutting-edge mathematical and statistical theory and the next generation of computational tools to address the above challenges with an emphasis on learning with sparsity and other emerging data models, and to potentially explore their applications in AI, including medical imaging, automated quality control, and self-driving cars, evaluated on both simulated and real data.

Understanding and exploiting structure in data to extract useful information from big datasets with the purpose of making optimal decisions will be the main research thrust for this position, where we investigate data models ranging from sparsity, to rank constraints, to deep network models, such as generative adversarial networks

The overall purpose for this PhD studentship is to develop new mathematical and statistical tools and computational methodology for sparse and structured signal processing, to establish their theoretical framework, and to explore their applications in AI. The student is also expected to join the collaborations from our ongoing AI-related projects.

Admission requirements
Prerequisites include 240 ECTS credits (swe. Högskolepoäng) of higher education studies of which 60 ECTS credits should be on an advanced level (Master’s level). In addition to these general requirements, the applicant is required to have completed at least 90 ECTS credits in mathematical statistics, of which at least 30 credits shall have been acquired at the advanced level (Master’s level). Applicants who in some other system either within Sweden or abroad have acquired largely equivalent skills are also eligible.

You have a degree of master of science in mathematical statistics, degree of master of science applied mathematics, master of science in engineering or an equivalent degree in a related field. Excellent programming skills (preferably MatLab, Python or R) are required. Good knowledge of English language, both written and spoken, are key requirements. Documented knowledge and experience in signal processing and image analysis are merit.

You are expected to play an active role in this interdisciplinary cooperation and have a scientific and result-oriented approach for your work. You should therefore have a very good communication and collaboration ability. You are structured, flexible and solution-oriented.

The assessments of the applicants are based on their qualifications and their ability to benefit from the doctoral-level education they will receive.

Applicants with a degree not from a Swedish university are encouraged to provide results obtained from GMAT (and/or GRE) and TOEFL/IELTS tests if available.

About the employment
The position is intended to result in a doctoral degree and the main task of the PhD student is to pursue their doctoral studies which include both participation in research and postgraduate courses. The duties can include teaching and other departmental work (up to a maximum of 20%). The employment is limited to four years of full-time (48 months) or up to five years for teaching part-time. Salary is set in accordance with the established salary ladder for PhD position. The employment starts in the autumn of 2019 or according to agreement.

A complete application should contain the following documents:

  • a personal letter with a brief description of your qualifications and your research interests. Motivate why you are applying for the employment and describe how your qualifications and merits are relevant to the employment,
  • a curriculum vitae,
  • reinforced copies of diplomas or equivalent, including documentation of completed academic courses, grades obtained, and possibly other certificates
  • copies of relevant work such as Bachelor’s or Master’s thesis or articles that you have authored or co-authored,
  • contact information for at least two reference persons.

The Department of Mathematics and Mathematical Statistics values ​​the qualities that an even gender distribution brings to the department, and therefore we particularly encourage women to apply for the position. You apply via our e-recruitment system Varbi. Log in and apply via the button at the bottom of the page. The deadline for applications is 2019-08-30.

Job location: 
MIT Building 3rd floor
90187 Umeå
Contact and application information
Friday, August 30, 2019
Contact name: 
Professor Jun Yu
Contact email: