PhD Position DC7 - GFZ Helmholtz Centre for Geosciences, Germany - Flood risk and adaptation un[...]

euraxess.ec.europa.eu - Jobboard; Deutschland; Vor Ort; EUR 30.000 - 40.000; Vollzeit

Zusammenfassung

A leading research institute in Germany is seeking a PhD candidate for a project focused on flood risk adaptation. This role involves developing an Agent Based Model to evaluate adaptive behaviors of SMEs under flood scenarios. The candidate will receive interdisciplinary training and engage in significant research to support policy development in resilience engineering. Fluency in English is required; German skills are beneficial.

Qualifikationen

• Master's degree in Environmental Sciences, Engineering, Geoinformatics or related fields.
• Experience with Agent Based Modelling.
• Expertise in machine learning and Bayesian statistics is beneficial.
• Capacity for interdisciplinary teamwork and excellent communication skills.
• Ability to communicate in English fluently.

Aufgaben

• Analyse flood damage and vulnerability data and develop an Agent Based Model.
• Couple the ABM to the Regional Flood Model.
• Develop a dynamic risk assessment tool for Critical Infrastructure resilience.
• Write well-documented, reusable code in Python or R.
• Present and publish research results.

Kenntnisse

Agent Based Modelling

Machine Learning

Bayesian Statistics

Interdisciplinary Teamwork

Fluent English

Ausbildung

Master's degree in Environmental Sciences, Engineering, or Geoinformatics

Tools

Python

R

Jobbeschreibung

Organisation/Company GFZ Helmholtz Centre for Geosciences Department Section Hydrology Research Field Engineering » Civil engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country Germany Application Deadline 31 Oct 2025 - 23:59 (Europe/Berlin) Type of Contract Temporary Job Status Full-time Hours Per Week 39 Is the job funded through the EU Research Framework Programme? Horizon Europe - MSCA Reference Number DC7 Marie Curie Grant Agreement Number 101225914 Is the Job related to staff position within a Research Infrastructure? No

Offer Description

Ageing infrastructures, urbanization, and climate change are intensifying the vulnerability of critical infrastructures (CI), high-tech industries (HTI), and communities to cascading hazards, particularly NaTech (Natural Hazard Triggering Technological) events. REUNATECH is a Horizon Europe Marie Skłodowska-Curie Doctoral Network that aims to educate and train the new generation of Doctoral Candidates (DCs) capable of tackling these challenges through cutting-edge interdisciplinary research and innovation. REUNATECH brings together leading universities, research institutes, and industrial partners across Europe to deliver a world-class doctoral training programme in risk assessment, resilience engineering, and smart technologies.

Its scientific vision targets: (1) the development of a holistic multi-hazard risk framework capturing cascading effects across systems and scales; (2) the creation of digital environments utilizing real-time data for dynamic risk evaluation; (3) the advancement of risk-to-resilience methodologies; and (4) the establishment of digital twin-based resilience frameworks for CI, HTI, and urban environments.

The network emphasizes innovation through the development of a Virtual Training Environment (VTE) for disaster response simulation, integration of Building Information Modelling (BIM) with Structural Health Monitoring (SHM) using smart sensor networks, and resilience-informed design platforms. Furthermore, REUNATECH promotes trans-disciplinary collaboration via multi-stakeholder platforms, bridging academia, industry, and policy. Through its holistic, digital, and resilience-centered approach, REUNATECH supports the European Green Deal, Sendai Framework, and UN Sustainable Development Goals. DCs will benefit from an immersive training structure comprising cross-sectoral secondments, workshops, and summer schools, enhancing their expertise in NaTech risk and resilience and enabling their integration into European and international research and innovation landscapes.

Research Project

The PhD project DC7 aims to develop and apply a coupled Agent-Based Model (ABM) and couple it to the Regional Flood Model (RFM) to evaluate the effect of adaptive behaviour of small and medium-sized enterprises (SMEs) and other agents on the development of flood risk in Germany. The ABM development draws on empirical data from the HOWAS21 damage database and other data sources. The coupled model system will be used to simulate diverse pathways of adaptation under varying flood risk conditions, including climate and economic scenarios, to assess the implications of adaptation decisions on temporal flood risk development. These projections will enable the identification of sustainable adaptation strategies and support policy development.

Objectives

• Develop an Agent Based Model (ABM) that describes flood risk adaptation decisions of flood endangered agents, e.g. SMEs under varying flood scenarios. Flood adaptation behaviours and decision-making patterns will be analysed using the HOWAS21 database and other empirical damage and vulnerability data.
• Couple the ABM with the Regional Flood Model (RFM) to describe temporal developments of flood risk considering adaptation decisions. Different adaptation pathways in flood-prone regions will be explored.
• Develop a dynamic risk assessment tool (together with other PhDs) to evaluate Critical Infrastructure resilience under multi-hazard scenarios, integrating flood adaptation and resilience modelling.

DUTIES AND RESPONSIBILITIES

• Analyse flood damage and vulnerability data and develop an Agent Based Model that describes adaptation decisions of flood endangered agents
• Couple the ABM to the RFM to describe temporal developments of flood risk considering adaptation decisions.
• Develop a dynamic risk assessment tool (together with other PhDs) to evaluate Critical Infrastructure resilience under multi-hazard scenarios
• Model development and data analyses includes writing well-documented, reusable code (Python or R)
• Collaborate closely with fellow PhDs and project partners in an interdisciplinary context
• Present, publish and communicate research results at scientific meetings and in scholarly journals
• Deliver according to the project timelines and university/department requirements

As the job descriptions cannot be exhaustive, the Researcher may be required to undertake other duties, which are broadly in line with the above duties and responsibilities.

Where to apply

E-mail [e-mail]

Requirements

Research Field Environmental science Education Level Master Degree or equivalent

Skills/Qualifications

• Master's degree in Environmental Sciences, Engineering, Geoinformatics or related fields
• Experience with Agent Based Modelling
• Expertise in machine learning, Bayesian statistics is beneficial
• Capacity for interdisciplinary teamwork and excellent communication skills
• Ability to communicate in English fluently (German language command is helpful but not mandatory)

Languages ENGLISH Level Good

Additional Information

Eligibility criteria

The applicant must not be in the possession of a doctoral degree or have entered a PhD program at the date of the recruitment.

At the time of recruitment, the researcher must not have resided or carried out their main activity (work, studies, etc.) in the country of their recruiting organization for more than 12 months in the three years imme-diately prior to the recruitment date. Compulsory national service and/or short stays such as holidays are not considered.

Selection process

The selection and recruitment process will be in accordance with the European Charter and Code of Conduct for the Recruitment of Researchers. The recruitment process will be open, transparent, impartial, equitable, and merit based. There will be no overt/covert discrimination based on race, gender, sexual orientation, religion or belief, disability or age. To this end, the following selection criteria will be considered:

• Academic performance (diplomas, university transcripts, etc.)
• Research and industrial experience
• Awards and fellowships
• Publications and patents
• Research, leadership, and creativity potential
• English knowledge
• Other relevant items based on the specific candidate

The application deadline is 31st October 2025 23:59 CET. All applications will be analysed after the application deadline, and the shortlisted candidates will be invited to a teleconference interview. At the end of the selection process, all the applicants will be informed of the outcome of their application by return email.

DISCLAIMER

By applying for this position, the applicants:

• Give their consent to circulate their application and personal data within the members of the consortium.
• Declare to fulfil the eligibility requirements defined by above.
• Agree to spend an academic secondment of 3 months as well as an industrial secondment of another 3 months within the REUNATECH consortium as described.
• Agree that they will comply with the planned PhD enrolment.

Each application must include the following material:

• Curriculum vitae setting out the educational qualifications as well as any additional scientific achievements and publications. The CV must clearly indicate the applicant’s vitae name, surname, gender, date of birth, nationality, country of residence in the last three years).
• Evidence of English proficiency.
• Copy of Bachelor’s and Master’s certificates.
• Copy of Bachelor’s and Master’s transcripts.
• Any additional material useful for the assessment of the candidate (e.g., recommendation letters, research project/statement in agreement with the requirements specified in previous text). All material must be included in one compiled pdf file. The file should be named indicating the surname of the applicant and (in order of preference) the three preferred positions for which the applicant is applying, i.e. Surname_Position1_Position2_Position3.pdf (e.g. Surname_DC1_DC7_DC10)

Applications will be accepted via email only after 1st September 2025 for data protection reasons. The one compiled pdf file should be sent via mail to the following email address: [e-mail]

Application sent before 1st September 2025 will not be considered.

Additional comments

The selected applicant will be enrolled into the Ph.D. program at the Humboldt-Universitaet zu Berlin, Geography Department to conduct the planned research activities.

Academic secondment

DC7 will undertake a 3-month research secondment at an academic partner of the REUNATECH project. The planned host institution for this secondment is TU Delft, The Netherlands.

DC7 will undertake a 3-month research secondment at an industrial partner of the REUNATECH project. The planned host institution for this secondment is Deutsche Rückversicherung, Düsseldorf, Germany.