🇳🇱 Amsterdam, Netherlands · 9h ago
PhD Candidate in AI‑Driven Flood Scenarios and Insurability Maps (Pillar 3)
University of Amsterdam
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Are you interested in AI applications in flood risk modelling, to answer questions that no existing dataset can address? Are you motivated by the challenge of generating credible extreme flood scenarios that lie beyond the range of historical observations and the current climate, so that we can describe the effects of climate change and the impact to the financial sector?Working at the UvAAre you our new colleague?We are looking for a PhD candidate to develop AI methods that extend current sets of flood scenarios derived from physical and numerical models, incorporate climate change effects, and then use these enriched datasets to assess the future insurability of flood risks. In addition, you will develop methods that use AI to produce rapid damage estimates for financial institutions in the event of an impending flood.About FIRMFlood Insurance and Risk Management (FIRM) is a five‑year research programme (2026–2030), funded by TKI Deltatechnologie, Nationale‑Nederlanden and contributions from more than 10 partners from academia, the financial sector, engineering consultancies and government. The central aim of FIRM is to estimate flood and water nuisance risk information as realistically as possible and to make it suitable for use in the financial sector.FIRM consists of four substantive research pillars and a work package on knowledge transfer. This vacancy concerns a PhD position within Pillar 3. You will collaborate closely with the PhD candidates in the other pillars, be supervised by researchers at the University of Amsterdam, and work together with experts from the field.A good salary Your salary will be determined on the basis of your experience and seniority, in accordance with the Labour agreement. Your grading and therefore the amounts mentioned are based on full employment of 38 hours per week. 232 vacation hours We also offer parental leave, partner leave and social engagement leave. Through the Employment Conditions Menu Card, there are opportunities for even more vacation hours.Financial arrangements On top of your salary, you receive holiday allowance (8%) and a year-end bonus (8.3%). You build up a good pension with ABP. And in addition, we offer many other allowances and schemes.Career development and room for growth The UvA encourages you to continue to professionalise through training courses and open UvA lectures.Family matters Relocating to the Netherlands with your family? Find out about daycare, schooling, health insurance or employment options and support for your partner or spouse. Including the IDCN-network which provides support for your spouse through various training sessions, social activities, and networking events. All About This VacancyThis is what you will be doingIn 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 What We Ask Of YouThis 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)This Is What We Offer YouWe 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 instituSourced from LinkedIn. Relocantly aggregates public job postings; apply on the original site.