This position is part of a large-scale project called UrbanAIR, funded by the European Union’s HORIZON Research and Innovation Programme under Grant Agreement number 101188131. UrbanAIR aims to develop a Digital Twin for Climate-Resilient Urban Planning in collaboration with partners from academia, research institutes, industry, and governmental organizations. This digital twin will support decision-makers in addressing urban design challenges related to atmospheric heat and air quality, ultimately enhancing the health, socio-economic well-being, and economic prosperity of citizens impacted by climate change. Through high-resolution modeling of complex atmospheric systems, UrbanAIR will provide essential tools for climate adaptation and hazard mitigation within urban planning and design. There will be frequent interactions with cooperation partners in Delft and Bergen including visits in The Netherlands and Norway. Futhermore there will be scientific exchanges with the other partners of the project spanned across Europe ( e.g., Spain, Germany, France, UK and Switzerlands, Netherlands, Norway). The full list of partners is: VITO, Barcelona Supercomputing Center, Delft University of Technology, KNMI, Imperial College London, Université Toulouse III, Météo - France, Freiburg University, Technical University Ilmenau, NORCE, Bern FachHochschule, Leibniz University Hannover, ARUP, UKRI, Cerfacs, Resilient-Cities Network, Future Needs and SMHI.

Please go to the webpage for details: https://www.urbanair-project.eu/

Your tasks

The position includes the following responsibilities:

• You are primarily engaged in mathematical research and algorithmic development, contributing to several work packages (WPs) within the Urban Air Project. This includes investigating a hierarchical cascade of uncertainties arising from various models and assimilated data, which influence and are simultaneously influenced by the underlying decision-making processes. Initially, this will be explored using toy models before being scaled up to address the larger problem at hand.
• You publish your findings in renowned journals, such as those in the SIAM series, and aim to complete your PhD within the fixed-term employment period.
• You actively participate in and contribute to the organization of events within the Urban Air Project, collaborating closely with researchers both in the project and are active in the Data Science Lab.
• You assist in supervising theses and engage actively in academic self-governance.
• You present your research at national and international conferences, workshops, and meetings, and undertake short stays with collaboration partners abroad, primarily in the Netherlands and Norway.

Your profile

For being part of the selection process is needed:

• A completed scientific university degree (Diploma (Univ.), Master or equivalent degree) and PhD in Mathematics, or in Computer Science with a mathematical emphasis, or in a related field, by the time of employment.
• Profound knowledge in data assimilation—particularly in particle filtering—as well as in data science, machine learning, and statistics, demonstrated by outstanding research and/or relevant publications in international journals.
• A solid command of the mathematical aspects of data science that are pertinent to both research and teaching.
• Excellent written and oral English skills.
• Very good to good programming skills, particularly in Python and MATLAB.

Following competences are desirable:

• Commitment, enthusiasm, an independent work style, initiative, and strong teamwork skills.

Please provide evidence of the qualifications required for the job by certificates and references.

What we offer you:

• attractive remuneration according to collective agreement (as per TV-L) incl. granting of a special annual payment
• vacation entitlement of 30 days in the calendar year and additional days off on 24 / 31 December
• family-friendly, flexible working time model
• VBL - pension scheme in the public sector
• wide range of individual training opportunities
• extensive health and sports programs
• respectful working environment at a renowned university