The internship is embedded in the framework of the international project PHOTOCLIM – Fragility of Marine Photosynthesis under Climate Change ( https://www.photoclim.org), which investigates the dynamics of primary production in the world ocean and marginal seas. A particular scientific challenge addressed in this research is the analysis and reconstruction of long in-situ time series of primary production, which are often affected by missing data, nonlinearity, and environmental variability. The project combines oceanographic observations, bio-physical knowledge, and advanced data science approaches. The assistant will be trained in modern machine learning techniques, with special focus on representation learning and algorithms for filling observational gaps in time series.
Machine learning; Primary production; Oceanography; Time-series analysis; Data reconstruction.
Erasmus + grant available depending on eligibility criteria of the sending institution
Data analysis and processing • Work with in-situ and satellite-based primary production datasets. • Pre-process and quality-control oceanographic time series. • Identify and handle missing or inconsistent data. Machine learning applications • Learn and apply methods of representation learning for time-series reconstruction.• Implement algorithms for gap-filling and prediction of primary production. • Compare performance of existing and newly developed models. • Investigate challenges of nonlinear dynamics in environmental time series. Scientific communication and collaboration skills
Scientific and analytical skills – ability to analyse in-situ and satellite oceanographic datasets and interpret results in the context of marine primary production. • Data processing and management – experience in cleaning, organizing, and visualising large environmental datasets. • Machine learning competencies – understanding and application of algorithms for time-series reconstruction, gap-filling, and prediction. • Programming and computational skills – use of Python and R for data analysis, model implementation, and result visualisation. • Critical and problem-solving skills – addressing challenges of nonlinearity and uncertainty in environmental data. • Interdisciplinary integration – linking biophysical oceanography with modern data science approaches. • Scientific communication – preparation of abstracts, reports, and presentations for international conferences. • Collaboration and networking – experience working in international, multidisciplinary research teams. • Autonomous research skills – capacity to plan and conduct independent research under mentorship.
Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Education and Culture Executive Agency (EACEA). Neither the European Union nor EACEA can be held responsible for them.
