Summary of the work plan

Marine ecosystems are complex adaptive systems that play a crucial role in supporting life on Earth. Monitoring and understanding the state of marine ecosystems is essential for effective environmental management and conservation efforts and to mitigate the effects of climate and social changes. The challenge is to identify and map ecosystem indicators to provide meaningful insights into ecosystem health and conditions. The use of machine learning approaches is now considered important to identify, test and evaluate these ecosystem indicators enabling better decision-making and planning for present and future scenarios.Machine learning can help to identify new indicators and refine existing one by identifying key thresholds or nonlinear relationships that may not be apparent using traditional statistical methods.

One of the key benefits of using machine learning in the analysis of ecosystem indicators is the ability to process vast amounts of data quickly and accurately. Ecosystems generate large amounts of data from various sources, such as remote sensing, process-based numerical models, citizen science, and monitoring networks. The workplan will include:  (1) development of machine learning algorithms to process these data sets and identify patterns, trends, and relationships that may not be immediately apparent to human analysts;(2) advanced neural network approaches to improve the accuracy of ecosystem indicators through data assimilation techniques able to handle the varying levels of accuracy and uncertainty; (3) Identification of novel indicators or the refinement of existing ones for coastal and marine ecosystem management.

Analyses will focus on Atlantic Ocean ecosystems for present and future scenarios. By leveraging large datasets, predictive modelling, data assimilation, and identification of novel indicators, the PhD research willenable more accurate, scalable, and cost-effective analyses of ecosystem state to support the sustainable management of the Atlantic Ocean resources. The long term aim is to improve our understanding of ecosystem dynamics, inform decision-making, and support effective ecosystem conservation and restoration efforts in the face of complex and rapidly changing environmental challenges.

The candidate will work with EC HORISON 2020/EUROPE projects Mission Atlantic (2020-2025;, and Horizon EU OceanICU with the support of PI’s from those international consortia.

Candidate Profile

The candidate will be selected based on his/her CV, LoS and accompanying documentation, and evaluated taking in consideration:

  • Background knowledge in modeling with machine learning approaches
  • Experience dealing with large datasets with preference for geophysical and ecological data including associated social systems
  • Experience with time series data analysis and predictive modeling techniques, on both spatial and temporal scales
  • Advanced programming skills e.g. Python, Matlab, R, etc.
  • Ability to work in group within an international network across different areas such as oceanography, data analytics, marine biology, ecosystem modeling

Name(s) of supervisor(s)

  • Supervisor: Isabela Pinto, CIIMAR /  Un Porto (accepted)
  • Co-Supervisor:   Prof. Patrizio Mariani, Head of Ocean Technology, Technical University of Denmark, Denmark
  • Co-Supervisor:   Lohengrin Fernandes, Head of Applied Biotechnology Division, Estudos do Mar Almirante Paulo Moreira (IEAPM), Brazil
  • Co-Supervisor:  Oscar Godoy, INMAR, Un Cadiz (confirmed)

Name(s) of host institution(s)

The research will be conducted at University of  Porto/CIIMAR Doctoral Programme, in cooperation with the co-supervisors research groups.

Identification of PhD program (at the Portuguese University)

Un Porto/CIIMAR Doctoral Programme in Marine Science, Technology and Management

Notice of the Call ( English version)

The AIR Centre PhD Scholarship Programme aims at training the leaders of the future

Enhancing scientific research and technology development capabilities of AIR Centre Network in order to better respond to national priorities and global challenges in the Atlantic region;

  • Strengthening existing collaboration ties and exploring or developing new collaboration ties between the AIR Centre and the Portuguese scientific community in areas of common interest;
  • Promoting bilateral/multilateral cooperation between Portuguese scientific institutions and other institutions from diverse Atlantic countries through inclusive knowledge and data sharing to promote job creation, young entrepreneurship and inclusive sustainable development;
  • Expanding the reach of the AIR Centre scientific agenda through a wider engagement with the academia to demonstrate the societal relevance and public value of research.

Doctoral research work will be carried out entirely or partially in a Portuguese institution.


Scholarships are annual, renewable to a maximum of 4 years.


Applications and all supporting documents must be submitted online using the Application Form available on each scholarship page. Applications submitted by other means will not be accepted.

Application Process

Please note that you will need the items listed below. To ease the submission process, we suggest you gather them before you start your application:

  • Copy of your Identification Document (ID card, passport);
  • Certificate degree and grades transcript.
  • Your Curriculum Vitae and saved as PDF;
  • A Motivation Letter
  • 2 Recommendation letters.

Notification of results

Evaluation results will be communicated to the email address provided by the candidates in the application form.

For  further information about this opportunity, click here.

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