Job Information
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: 6 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.
Applications must be submitted by email to jose.matos@tecnico.ulisboa.pt, with gabriela.cunha@tecnico.ulisboa.pt in copy.
Where to apply
E-mail: jose.matos@tecnico.ulisboa.pt
Requirements
Research Field: Engineering » Civil engineering
Education Level: Master Degree or equivalent
Skills/Qualifications
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.
Additional Information
Benefits
Monthly maintenance allowance: According to the values for Research Fellowships awarded by FCT in Portugal (https://www.fct.pt/fct-atualizou-o-valor-das-bolsas-para-2026/), the amount of the monthly maintenance allowance is 1359,64€, being the payment method an option of the Fellow by Wire Transfer.
Eligibility criteria
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: Prof. José Pedro Gamito de Saldanha Calado Matos – nº mec. IST151906; Prof. Rodrigo De Almada Cardoso Proença de Oliveira – nº mec. IST126343; Prof. Diogo Silva Mendes – nº mec. IST176657
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 .
Workplace: The work will be developed at Instituto Superior Técnico (IST-ID, Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento), associated with the CERIS (Civil Engineering Research and Innovation for Sustainability) research center, under the scientific supervision of Professor José Pedro Matos.
Website for additional job details: https://ist-id.pt/recursos-humanos/bolseiros/#bolsas-ativas
Work Location(s)
Number of offers available: 1
Company/Institute: IST-ID
Country: Portugal
Contact
City: Lisbon
Website: http://ist-id.pt/
Street: Av. António José de Almeida 12
Postal Code: 1000-043 Lisbon
E-Mail: cristinadeoliveira@ist-id.pt