Institute for RemunerationEG 13 ID: 8726WB2 Start of employment: as soon as possible Application deadline 20.04.2026 Scope full time and fixted-term limited until 4 years

The Institute of Global Water Security conducts research on hydroclimatological extremes, stochastic hydrology, and environmental risk modelling. Our work combines probabilistic modelling, machine learning, and physical process understanding to develop operational tools for climate adaptation and water security.

The doctoral project focuses on the development of next-generation flood forecasting systems integrating machine learning, hydrological modelling, and real-time data assimilation. The research will explore sequence-based neural networks such as LSTM architectures, hybrid physics–machine learning approaches, and uncertainty quantification methods to improve river discharge prediction and flood early-warning systems.

The project also includes the integration of discharge forecasts with hydraulic modelling tools to generate dynamic inundation maps. The goal is to develop robust forecasting frameworks capable of supporting operational decision-making during extreme hydrological events.

The position is embedded in an international research environment and offers strong opportunities for collaboration, scientific publishing, and conference participation.

YOUR TASKS

Develop and implement LSTM-based and hybrid machine learning models for flood forecasting
Design and evaluate data assimilation schemes for real-time updating of hydrological predictions
Integrate hydrological forecasts with hydraulic modelling tools to generate inundation maps
Quantify predictive uncertainty and evaluate model reliability during extreme flood events
Publish scientific results and contribute to teaching activities of the institute

YOUR PROFILE

Requirements

Completed scientific university studies (master's degree or equivalent), in particular in the field of hydrology, water resources engineering, environmental engineering, civil engineering, computer science, or a related discipline

Desired knowledge and personal skills

Strong programming skills in Python and experience with ML frameworks (PyTorch, TensorFlow or similar)
Background in hydrological modelling, time-series analysis, or environmental data science
Knowledge of machine learning for sequential data (LSTM, RNN, transformers, or related methods)
Experience handling large environmental or geophysical datasets
Strong analytical skills and ability to work independently in interdisciplinary research environments

OUR OFFER

Integration into a growing international research institute
Close supervision and strong support for PhD completion
Access to high-performance computing resources
Opportunities to publish in leading scientific journals and attend international conferences
Flexible working conditions within a collaborative research environment

At the heart of TU Hamburg’s research, teaching and transfer of technology is the guiding principle of developing technology for people. The TU Hamburg sees itself in this context as a family-friendly and sustainable university with high performance and quality standards that strives for excellence in all its research fields. Interdisciplinarity, innovation, regionality and internationality are binding principles in our actions. With currently around 8.000 students, 110 professors and 1.650 employees, the TU Hamburg is characterised by short decision-making processes and close cooperation between the board, the institutes, the deans of studies, the research areas and the administration. We identify ourselves with a modern leadership culture and cultivate appreciative interaction.

For further information please contact Prof. Simon Papalexiou, Tel.-Nr. +49 40 30601 3107, Email: .

We value diversity, therefore all applications are welcome, regardless of gender, gender identity, ethnic origin, nationality, age, religion and belief, disability, sexual orientation and identity or social background.

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Please send your complete application documents (cover letter, curriculum vitae in table form, proof of completed training and/or university degree, job references or certificates of employment) via the online application system.

Notice for graduates of foreign educational qualifications:
Please submit proof of all obtained university degrees and, if available, the recognition of your educational qualifications in Germany (e.g. anabin excerpts and/or acknowledgement of previous employers).