Postdoctoral Position (two years) in Optimization for Statistical Learning - Befattningen är under tillsättning eller redan tillsatt, och det är inte längre möjligt att lämna in en ansökan

The Department of Mathematics and Mathematical Statistics at Umeå University is opening a postdoctoral position in mathematical statistics within the centre Wallenberg AI, Autonomous Systems, and Software Program (WASP) with an emphasis on optimization for statistical learning. Last day to apply September 30, 2019.

The appointment is for two years at the Department of Mathematics and Mathematical Statistics. The successful candidate is expected to conduct excellent research, actively engage with collaborators, and to participate in the daily activities of the research environment. Starting date is fall 2019 or as otherwise agreed.

Background
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 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. This project is part of this program. 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 working 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 data into information, predictions and intelligent decisions.

Optimization theory is vital to modern statistical learning and is at the forefront of advances in AI. The main objective of this postdoctoral position is to develop the next generation of optimization tools to address the above challenges in the context of modern statistical learning, and potentially explore their applications in AI, including medical imaging, automated quality control, and self-driving cars, evaluated on both simulated and real data.

Within this broad framework, the successful candidate is encouraged to develop its own research agenda, in close collaboration with mentors and colleagues. Potential areas of interest include, but are not limited to:

• Training generative adversarial networks
• Nonconvex algorithms for linear inverse problems (such as compressive sensing)
• Robust optimization and defence against adversarial examples in deep neural net
• Role of over-parametrization in training and generalization of deep neural nets
• Global geometry of nonconvex problems
• Efficient and scalable algorithms for constrained nonconvex optimization
• Application of Langevin dynamics and other Monte-Carlo techniques in optimization
• Online and storage-optimal algorithms for large scale convex optimization

Qualifications
The applicant must have earned a PhD or a foreign degree that is deemed equivalent to a PhD in mathematical statistics, applied mathematics or equivalent academic competence. Applicants must have completed their Ph.D. within three years of the application deadline but exceptions will be considered in case of illness, parental leave, clinical practice, positions of trust within labour unions, or other similar circumstances.

Documented knowledge and proven experience in modern statistical learning and optimization theory are required. In addition, excellent programming skills (preferably MatLab, Python and R) and excellent communication skills in written and spoken English are required.

The research tasks require great independence, accuracy and dedication. Documented scientific momentum and the ability to work independently as well as part of a research group are merits.

In particular, excellent track record of publication and experience in optimization for statistical learning are strong merits. Experience of interdisciplinary research projects and cross-disciplinary collaboration, in particular within the specific application area, is qualifying.

Application

The complete application should contain the following documents:

• A cover letter (one page) describing yourself, your previous research achievements, your preferred area of scientific application, and your motivation for your interest in this position
• A Curriculum Vitae with a list of publications
• Reinforced copies doctoral degree certificate and other relevant degree certificates as well as relevant grades
• Copies of (maximum of five) relevant publications plus your PhD thesis
• A research plan, 3-4 pages in length, which describes your research interests and how to contribute to the research project
• Contact details to two reference persons, familiar with your qualifications
• Other possible documents you wish to claim

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.

The application, including attachments, should be written in Swedish or English. 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 September 30, 2019.

Further information
Further information is provided by Professor Jun Yu, +46-(0)90-786 51 27, jun.yu@umu.se and Assistant Professor Armin Eftekhari, armin.eftekhari@umu.se. You can also contact the Head of department Åke Brännström for additional questions at ake.brannstrom@umu.se

We look forward to receiving your application!

Research at the Department of Mathematics and Mathematical Statistics is conducted within mathematics, mathematical statistics and computational science. Important cooperation partners include the Faculty of Science and Technology, the Faculty of Medicine, Umeå School of Business and Economics, Umeå School of Sport Sciences, the University Hospital, the Faculty of Forest Sciences at the Swedish University of Agricultural Sciences, as well as public authorities and industry. We provide education at all levels with a particular focus on civil engineering programs.

For more information see https://www.umu.se/en/department-of-mathematics-and-mathematical-statistics/