Job offer
Role details
Job location
Tech stack
Job description
- Organise and implement household surveys (partly longitudinal surveys) in at least two of the following case study areas (Rhine, Mekong, Pungwe, Upper Mississippi)
- Combine the new survey data with data from past surveys, analyse the data using machine learning approaches and develop a global flood damage model
- Establish (semi-)automatic processes for analysing survey data, including writing well-documented, reusable code (Python or R)
- Collaborate closely with project partners in an interdisciplinary context
- Present, publish and communicate research results at scientific meetings, conferences, and in scholarly journals
- Undertake a PhD study at Humboldt University Berlin
- Deliver according to the project timelines and university/department requirements
Requirements
In the section Hydrology, we are seeking a motivated and talented PhD candidate to work in the ERC Synergy Grant Project "LIMIT2ADAPT - When and where do we reach the limits of adaptation to riverine flood risk?".
You have experience in machine learning, programming and flood risk research. If so, we encourage you to apply!
You will conduct household surveys in international case study areas, analyse the data and develop a global flood damage model. Your aim is to gain a better understanding of the factors influencing households' adaptation decisions and how these decisions affect their physical vulnerability and flood risk. You have the interest and ability to participate in collaborative and interdisciplinary research activities and discussions in the field of adaptation and flood risk. You will closely collaborate with external partners, and present, publish and communicate research results at international conferences and in scholarly journals., * Master's degree in Environmental Sciences, Physical Geography, Engineering, Geoinformatics or related fields
- Understanding of flood risk system: hazard, exposure, and vulnerability
- Programming skills (Python, R)
- Capacity for interdisciplinary teamwork and excellent communication skills
- Ability to communicate in English fluently
Desirable Qualifications:
- Experience with surveys or survey data analyses is beneficial
- Expertise in machine learning and Bayesian statistics is beneficial
Benefits & conditions
- Work on a scientifically exciting, socially highly relevant, and globally visible ERC Synergy Grant project
- Collaborate with leading scientists and their research groups
- Take on ambitious and diverse tasks in a dynamic, international research environment
- State-of-the-art equipment
- Public service benefits
- Extensive training opportunities
- Professional career advice offered by our in-house Career-Centre
- Flexible working hours and conditions
- Support with finding a good work-life balance offered by benefit@work
- Institute day-care centre on site
- Working at the Albert Einstein Science Park on the Telegrafenberg in Potsdam
- Workplace within walking distance of Potsdam main train station, or just a short ride on the shuttle bus, Salary: The position is classed as salary group 13 according to "TVöD Bund (Tarifgebiet Ost)". The salary group is determined on the basis of the Collective Wage Agreement and the respective personal qualifications. Working hours: Part-time 75% (currently 29.25 h/week)