PhD students for Model-Data Fusion on Complex Networks

Conteúdo do emprego

Empa - the place where innovation startsEmpa is the research institute for materials science and technology of the ETH Domain and conducts cutting-edge research for the benefit of industry and the well-being of society.The Laboratory of Multiscale Studies in Building Physics focuses on developing methodologies and technologies that support energy transition and a sustainable society by improving the performance of porous media and building materials, increasing the use of renewable energy sources, and ameliorating the quality of the built environment both for human comfort and public health. More recently, the laboratory has pursued model development and observational studies for investigating epidemic dynamics. The outcomes of these research activities have contributed to the forecasting and mitigation of the COVID-19 pandemic in Switzerland, receiving considerable mass-media coverage. The laboratory has excellent experimental infrastructures (including cutting-edge experimental fluid dynamics facilities, environmental chambers, and various equipment to investigate processes in porous materials) and an extensive experience in investigating coupled multiphysics phenomena and complex multiscale processes, as well as in developing and using cutting-edge scientific computing techniques (including HPC, machine learning, multiscale algorithms).We invite applications for twoPhD students for Model-Data Fusion on Complex NetworksThe doctoral researchers will focus on the development of stochastic and data-driven methodologies to improve model predictivity and guide intervention measures on multiscale networks. In particular, the applications will deal with disease spread in human populations and cascade failure in power networks. The project will tackle fundamental challenges arising from modeling large-scale networks and integrate original ideas across machine learning and stochastic modeling to enable efficient yet high-fidelity predictions of complex network dynamics.We are looking for two PhD students with strong analytical background, and MSc degree in Engineering, Applied Mathematics, Physics, or a related field. Professional command of English (both written and spoken) is mandatory. The successful candidates show enthusiasm for conducting original research and strives for scientific excellence. Prior exposure to stochastic models, machine learning, or network science would be highly desirable.Work location is at the Laboratory of Multiscale Studies in Building Physics in Dübendorf, Switzerland. The PhD students will be enrolled in the EPFL doctoral school, under supervision of Prof. Jan S Hesthaven at the Chair of Computational Mathematics and Simulation Science https://www.epfl.ch/labs/mcss/ .We offer internationally competitive conditions, optimal computational and experimental facilities, and the opportunity to work in a multidisciplinary environment where communication and interaction to create synergies and develop novel ideas are highly valued. The positions will be available as soon as possible or upon agreement; the planned project is four years.For further information about the positions please contact, Dr Hossein Gorji (Scientist and Principal Investigator) mohammadhossein.gorji@empa.ch , Dr Ivan Lunati (Head of Laboratory at Empa) ivan.lunati@empa.ch , or Prof. Jan S Hesthaven (Chair Professor at EPFL) jan.hesthaven@epfl.ch , and visit our websites https://www.empa.ch/web/s305 , https://www.epfl.ch/labs/mcss/ and Empa-VideoWe look forward to receiving your online applicationuntil 30 November 2022 including a letter of motivation, CV, diplomas with transcripts of all obtained degrees, a copy (either a link or upload) of the master thesis and additional publications (journal or conference papers) if available, and the contact details of two referees. Please upload the requested documents through our webpage. Applications via email will not be considered.Empa, Patricia Nitzsche, Human Resources, Ueberlandstrasse 129, 8600 Dübendorf, Switzerland.