In many countries, a set of flood scenarios for the current climate is developed using hydraulic models. These scenarios describe the consequences of a flood in terms of flood depth, flood extend, economic damage and loss of life. These scenarios are constructed for a given load (a water level on sea or rivers, or river discharge, with an associated probability of occurrence). Using these probabilities and the consequences, flood risk can be described. The current datasets, developed with physical and numerical models, are the result of decades of hydraulic modelling. These models are used to answer water management questions; applications in the financial sector raise new questions and requires different and additional scenarios. There are gaps in the probability domain between normative events for water systems and  stress tests events, and from an ESG and stress‑testing perspective there is a need to understand different scenarios of climate change (based on IPCC scenarios) in a specific year — these scenarios often do not yet exist. This PhD project develops AI methods to close these gaps so that flood impacts are better represented in climate analyses. The research consists of:

• 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.

Main duties and responsibilities:

• 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.

This is an interdisciplinary project that combines machine learning and AI, probabilistic risk modelling, hydrology and actuarial science. We realize that candidates will usually have expertise in one of these fields and ask for a genuine interest in the others.

• 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.

Applications that do not satisfy all listed requirements will not be taken into consideration.

Supervisors: Dr. Inez Zwetsloot (UvA) · Prof. dr. Bas Kolen (UvA, HKV) · Prof. dr. Drona Kandhai (UvA)

We offer:

• 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).

The collective labour agreement of Dutch universities applies.

The UvA has an extensive package of fringe benefits, including:

• 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.

This is where you’ll be working

The University of Amsterdam (UvA) is the largest university in the Netherlands and ranks among the top universities in Europe. With over 42,000 students, 6,000 staff and 3,000 PhD candidates, the UvA is an intellectual hub driven by a culture of curiosity.

The UvA is an equal‑opportunity employer. We value diversity and are committed to creating an inclusive environment for everyone.

About UvA Economics and Business

Education and research at UvA Economics and Business (EB) covers a wide range of fields that includes economics, data science, business administration, business analytics, accountancy, control, actuarial science, econometrics, finance and entrepreneurship. This is organised in 2 Schools: the Amsterdam Business School (ABS) and the Amsterdam School of Economics (ASE). Over 7,000 students are enrolled in our UvA Economics and Business programmes and around 500 employees provide education and support.
The Amsterdam School of Economics (ASE) enjoys international recognition for its research in actuarial science, econometrics, mathematical finance and quantitative economics.
The vacancy is within the Quantitative Economics section of the ASE, which is responsible for teaching most quantitative economics related courses at the undergraduate and graduate level. The section produces internationally acclaimed research and covers three different research programs: Actuarial Science & Mathematical Finance, Econometrics, and Equilibrium, Expectations & Dynamics (CeNDEF).

Find more information about Economics and Business on uva.nl/eb.

Find more information about the Amsterdam School of Economics on ase.uva.nl.

Find more information about the Quantitative Economics section here.