Umeå University, Faculty of Science and Technology

Umeå University is one of Sweden’s largest higher education institutions with over 37,000 students and about 4,700 employees. The University offers a diversity of high-quality education and world-leading research in several fields. Notably, the groundbreaking discovery of the CRISPR-Cas9 gene-editing tool, which was awarded the Nobel Prize in Chemistry, was made here. At Umeå University, everything is close. Our cohesive campuses make it easy to meet, work together and exchange knowledge, which promotes a dynamic and open culture.

The ongoing societal transformation and large green investments in northern Sweden create enormous opportunities and complex challenges. For Umeå University, conducting research about – and in the middle of – a society in transition is key. We also take pride in delivering education to enable regions to expand quickly and sustainably. In fact, the future is made here.

Are you interested in learning more? Read about Umeå university as a workplace

At the Department of Physics, strong and expanding research is conducted in several different research subjects, e.g. organic electronics, nanotechnology, photonics, space physics and theoretical physics. The department is part of the Chemical Biological Centre at Umeå University. We have a strong focus on interdisciplinary research and excellent access to modern instrumentation and infrastructure for analysis.

The Department of Physics is looking for an exceptional Postdoc to join our interdisciplinary group at Integrated Science Lab, IceLab. This is a two-year full-time position. Application deadline is March 1, 2018.

IceLab
IceLab is a physical hub for interdisciplinary research hosted by the departments of Ecology and Environmental Science, Mathematics and Mathematical Statistics, and Physics. We are about 20 researchers with different backgrounds. Together we work on solving life science problems with tools from physics, mathematics, and computer science. For the love of new ideas, we seek to connect researchers from different disciplines by organizing innovative and creative events. 

Project description
To understand and counteract pandemics, climate-driven changes in species distribution, and other important phenomena in complex systems, researchers use flow-based network models. Today researchers rely on memoryless one-step models that, for example, only take into account the current location of a traveller. However, this conventional approach ignores that the flow direction often depends on more than a single step, that is, where the flows come from or the state of the system. Recent evidence suggests that such higher-order information about real flow pathways is critical for capturing all-important phenomena in the dynamics and function of the system. This evidence exposes a shortcoming of conventional approaches and raises a major scientific question: How can we comprehend the higher-order effects of flow pathways in a barrage of data to understand the continuously changing organization of social and biological systems?

We are now seeking a Postdoc to join our interdisciplinary research group and develop powerful modelling and mapping tools for efficient analysis. Building on our work on multilayer and memory networks, we want to develop principled model selection methods to balance under- and overfitting of temporal interaction data, and visual interactive tools for exploring and communicating the results. This will allow us to take advantage of today's data explosion for revealing important organizational structures in complex systems and make it possible to address applied research questions in new ways concerning, for example, seasonal flu, and the changing species distribution on earth. IceLab offers a thriving, creative environment for interdisciplinary research and plentiful opportunity to explore and realize other projects as well. 

The position is a two-year full-time employment that will open in the spring of 2018, exact date according to agreement. 

Competence requirements
You should have a PhD degree, or a foreign degree that is deemed equivalent, in applied mathematics, computer science, physics, or relevant field. To be eligible, the degree should have been completed a maximum of three years before the end of the application period unless certain circumstances exist.  

Documented expertise in network science, machine learning or data visualization is required. You should have excellent programming skills using modern programing languages. You are highly motivated, responsible and passionate about developing as an individual scientist as well as a research team member. Personal qualities such as collaboration, communication, and analytical skills are essential. The candidate should have strong interest in interdisciplinary research, and must be proficient in spoken and written English. 

The application
The application should include:
1.
A cover letter summarizing your qualifications, your scientific interests, and your motives for applying (max 2 pages),
2.
A curriculum vitae (CV) with a publication list,
3.
Copies of relevant degree certificates,
4.
Copies of the PhD thesis and relevant publications,
5.
Names and contact information of two references. 

Applications must be submitted via e-recruitment system Varbi no later than 2018-03-01.

Information
For more information, contact Martin Rosvall, martin.rosvall@physics.umu.se
Research: http://www.tp.umu.se/~rosvall
Mapping tools: http://mapequation.org
IceLab: http://www.org.umu.se/icelab/english/

We look forward to receiving your application!

Type of employment Temporary position
Contract type Full time
First day of employment Spring of 2018, exacte date according to agreement
Salary Monthly salary
Number of positions 1
Full-time equivalent 100%
City Umeå
County Västerbottens län
Country Sweden
Reference number AN 2.2.1-23-18
Contact
  • Martin Rosvall, 070-2391973
Union representative
  • SACO, 090-786 53 65
  • ST, 090-786 54 31
  • SEKO, 090-786 52 96
Published 10.Jan.2018
Last application date 01.Mar.2018 11:59 PM CET

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