Research Studentships for PhD students - (BL157/2026-IST-ID)

Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID; Portugal; On-site; EUR 13,000 - 20,000; Full time

Job summary

IST-ID, em parceria com APA/IPMA, oferece uma Bolsista de Investigação de 6 meses, renovável até 18 meses, para o projeto PREDICT, focado na previsão probabilística de caudais e na operacionalização em tempo real.

Requisitos: mestrado em Engenharia Civil/Engenharia Ambiental, doutoramento, forte background em hidrologia, Python (NumPy, pandas, xarray, PyTorch), SQL/PostgreSQL, Git, Docker, inglês, capacidade de trabalho independente e em equipa multidisciplinar.

Qualifications

• Master’s degree in Civil Engineering, Environmental Engineering, or a closely related field and enrolment in a PhD programme.
• Solid background in hydrology and water resources.
• Programming experience, preferably in Python with scientific libraries.
• Knowledge of databases (SQL/PostgreSQL) and Git.
• Experience with hydrometeorological data and/or hydrological models is a plus.
• Familiarity with web services, APIs, and Docker containerisation.
• Good scientific writing and proficiency in English.

Responsibilities

• Research activities within the PREDICT project framework and collaboration with partners.
• Development of scientific skills through courses and publications.
• Preparation of publications in international peer-reviewed journals and conference participation.
• Documentation and reporting associated with PREDICT in collaboration with partners.

Skills

Hydrology and water resources

Python programming

SQL / PostgreSQL

Git version control

Hydrometeorological data

Web services / APIs

Docker containerisation

English proficiency

Multidisciplinary teamwork

Education

Master's degree in Civil or Environmental Engineering or related field

Tools

Python

NumPy

pandas

xarray

PyTorch

Job description

Organisation/Company Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID Department DRH Research Field Engineering » Civil engineering Researcher Profile First Stage Researcher (R1) Positions Master Positions Application Deadline 23 Nov 2026 - 23:59 (Europe/Lisbon) Country Portugal Type of Contract Not Applicable Job Status Not Applicable Offer Starting Date 26 Oct 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Reference Number LISBOA2030-FEDER-00856400 Is the Job related to staff position within a Research Infrastructure? No

Offer Description

Applications are open for 1 (one) Research Studentship, within the framework of project PREDICT – Probabilistic Real-time Estimation of Discharges Integrated with Cutting-edge Transformers (LISBOA2030-FEDER-00856400 | 1801P.01563), IST-ID, Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento, financed by national funds through FCT/MCTES (PIDDAC) and co-financed by the European Regional Development Fund (FEDER), under the Regional Program Lisboa 2030

Workplan: The candidate will develop activities within the PREDICT Scientific Research & Technological Development Project, a collaboration between IST, the Portuguese Environment Agency (APA) and the Portuguese Institute for Sea and Atmosphere (IPMA), which aims to set up a probabilistic streamflow forecasting and decision-support system for Portugal based on state-of-the-art machine learning models (e.g., Temporal Fusion Transformers). The work will focus mostly on the operationalization of streamflow forecasting systems, i.e., on turning trained models and probabilistic methods into a reliable, automated forecasting chain running in real time. The key topics (and the candidate's potential line of research) are:

• Co-development of the operational probabilistic forecasting pipeline, including the automated acquisition, validation and pre-processing of hydrometeorological observations and meteorological forecasts.
• Bias correction of meteorological forecasts to ensure consistency with the datasets used for model training, taking lead-time dependencies into account.
• Integration of deep learning models (Temporal Fusion Transformers) and statistical uncertainty models (Generalized Pareto Uncertainty) into the operational forecasting chain.
• Deployment, testing and monitoring of the online forecasting platform (building upon the Tethys Forecasting System), including forecasting and decision-support modules.
• Verification of operational forecasts using probabilistic performance metrics (e.g., CRPS, reliability, resolution).

The main tasks of the candidate are expected to be:

• Research (literature review and new advancements, including programming and software development).
• Development of scientific skills (e.g., by attending relevant courses).
• Preparation of publications in international peer-reviewed journals and participation in specialized conferences (talks / posters).
• Documentation, reporting and representation associated with PREDICT, in collaboration with the project partners (APA and IPMA).

Duration: The research studentship will have a duration of 6 months, scheduled to start in January 2027, and may be renewed up to a maximum of 18 months (until June 30, 2028, end date of the project), including the duration of the initial contract.

It is mandatory to formalize applications with the submission of the following documents: i) B1 Form – Fellowship application ( https://ist-id.pt/recursos-humanos/bolseiros/#documentos-relacionados ); ii) Curriculum Vitae ; iii) academic degree certificate, where applicable; iv) proof of enrollment in a PhD programme (or proof of application, if enrollment is not yet completed); v) motivation letter.

Admission Requirements

Applicants must hold a Master's degree in Civil Engineering, Environmental Engineering, or a closely related field, and be enrolled in a PhD programme.

The ideal candidate should have:

• A solid background in hydrology and water resources.
• Programming experience, preferably in Python (experience with scientific libraries such as NumPy, pandas, xarray or PyTorch is valued).
• Knowledge of databases (e.g., SQL/PostgreSQL) and version control (e.g., Git).
• Experience with hydrometeorological data (ground observations, reanalyses or numerical weather prediction products) and/or hydrological models will be considered an asset.
• Familiarity with web services and APIs, containerisation (e.g., Docker).
• Good scientific writing skills and proficiency in spoken and written English.
• Strong problem-solving abilities, with the capacity to work both independently and as part of a multidisciplinary team that includes external partners.

Admission Requirements

Applicants must hold a Master's degree in Civil Engineering, Environmental Engineering, or a closely related field, and be enrolled in a PhD programme.

The ideal candidate should have:

• A solid background in hydrology and water resources.
• Programming experience, preferably in Python (experience with scientific libraries such as NumPy, pandas, xarray or PyTorch is valued).
• Knowledge of databases (e.g., SQL/PostgreSQL) and version control (e.g., Git).
• Experience with hydrometeorological data (ground observations, reanalyses or numerical weather prediction products) and/or hydrological models will be considered an asset.
• Familiarity with web services and APIs, containerisation (e.g., Docker).
• Good scientific writing skills and proficiency in spoken and written English.
• Strong problem-solving abilities, with the capacity to work both independently and as part of a multidisciplinary team that includes external partners.

Selection process

Selection methods: The selection methods will be the following: analysis of the academic record (50%: M.Sc. grade – 20%; grades in Hydrology and Water Resources courses, or equivalent – 15%; grades in Probability and Statistics, or equivalent – 15%) and individual interview (50%, in person or online, to be scheduled with the jury). In the interview, programming skills, experience with hydrometeorological data and automated/operational workflows, proficiency in the English language and motivation for the topic, among other hard and soft skills, will be valued.

Composition of the selection Jury

Announcement/ notification of the results

The final evaluation results will be communicated to all applicants by email.

Deadlines and procedures of complaint and appeal

A complaint may be lodged from the final decision within 15 working days, or an appeal to the Executive Board of IST-ID within 30 working days, both counted from the respective notification

Additional comments

Legislation and Regulations

Statute of Scientific Research Fellow, approved by Law nr. 40/2004, of August 18, as worded by Decree-Law nr. 123/2019, of August 28; FCT Regulation for Research Studentships and Fellowships, available on https://dre.pt/application/file/a/127230968 .