PhD Candidate in AI‑Driven Flood Scenarios and Insurability Maps (Pillar 3)

Official university vacancy imported from University of Amsterdam. Details are presented for discovery and may change. Confirm the latest requirements and apply on the official source.

• Developing hybrid AI methods to extend and improve existing sets of flood scenarios with additional events that fill gaps in the existing dataset of scenarios, incorporate non‑linear effects such as failure of regional flood defences, and account for uncertainty in breach growth. In this way, flood impacts can also be described probabilistically and later added to the model of Pillar 1;
• Translating the current scenario sets into future climate states (using climate scenario as KNMI’23 and new insights of IPCC) and climate adaptation pathways for arbitrary horizon years, enabling the financial sector to quantify climate effects on portfolios;
• Building AI methods to integrate the flood scenarios to 1) early warning: a rapid operational damage assessment using flood and weather forecasts to support emergency warnings by insurers and 2)
• Future risk assessments by long‑term insurability maps for both data‑rich and data‑scarce contexts to identify potential future tippings points for insurance;
• Applying and validating the methods in case studies in the Netherlands and internationally, in collaboration with project partners.
• Developing hybrid AI methods to extend existing sets of flood scenarios for dike breaches and extreme precipitation, and to model the impacts probabilistically;
• Translating these scenario sets into future climate states for specific horizon years and emission pathways (KNMI’23);
• Building AI methods for rapid operational damage estimation and for strategic long‑term classification of insurability;
• Validating methods in both data‑rich areas (such as the Netherlands) and data‑scarce regions;
• Integrating results with the risk models of Pillars 1 and 2;
• Presenting at international conferences and publishing in leading peer‑reviewed journals;
• Completing a PhD thesis within four years;
• Contributing to teaching activities (approximately 20% of your time), including tutorials and supervision of Bachelor’s theses.
• A Master’s degree (or completion before September 2026) in Computer Science, Artificial Intelligence, Applied Mathematics, Hydrology, Civil Engineering, Actuarial Science or a related field;
• Strong programming skills (Python; familiarity with deep learning frameworks such as PyTorch or TensorFlow is an advantage);
• Affinity with probabilistic modelling and spatial data analysis;
• Interest in the physical behaviour of water systems and hydrology (prior knowledge is an asset but not a strict requirement);
• Ability to communicate AI model outputs to non‑specialists in the financial sector and government;
• Fluency in English is essential. Proficiency in Dutch is an asset;
• Intellectual curiosity, attention to detail and the drive to successfully complete a four‑year research project.
• A temporary employment contract for 38 hours per week for a duration of 4 years (initial contract of 18 months, extended upon positive evaluation). The position should lead to a PhD thesis;
• A gross monthly salary ranging from € 3,204 (year 1) to € 4,051 (year 4), scale P, excluding 8% holiday allowance and 8.3% year‑end bonus. The UFO profile “PhD Candidate” applies. The 30% ruling may apply to non‑Dutch candidates;
• An ambitious, international and collegial research environment within the Actuarial Science and Mathematical Finance research group at the Amsterdam School of Economics, in the heart of Amsterdam;
• A position in a large national research consortium, with direct access to unique insurance claims data and hydraulic model sets;
• Collaboration with leading financial institutions and water management organizations, with regular opportunities to present findings to practitioners;
• Access to the broad range of training, career and development opportunities at the UvA, including the support programme “Mastering my PhD” (for doctoral candidates).
• 29 days' holiday with full employment & extra holidays between Christmas and the New Year;
• Excellent work (from home) facilities;
• reimbursement of commuting expenses;
• pension accrual with ABP;
• In addition, the UvA offers excellent study and development opportunities and encourages employees to continue their professional development;
• You may participate in the open UvA lectures with which you can earn up to 30 credits per year.
• A motivation letter (maximum 1 page) explaining why you are interested in this specific pillar and outlining your relevant background;
• A full curriculum vitae;
• Transcripts of Bachelor’s and Master’s degrees;
• Names and contact details of two academic referees (this may include Master’s thesis supervisors).
• We would like to receive a cover letter and resume as a pdf file.
• The response period closes on 17 September 2026. Applications that we receive after the response deadline, that are not sent through the apply button or are not complete, we unfortunately cannot consider.
• The first round of interviews will take place during the week commencing 5 October 2026.
• In case of equal suitability, internal candidates will be given priority.