진학프로석·박사 채용 접수 플랫폼

REDUCE will unify stakeholder efforts and utilize an interdisciplinary scientific approach to reduce bycatch of marine megafauna and inform sustainable fishery management in the Eastern Central Atlantic Ocean. The diagnosis and integration of bycatch data across all industrial European fleets in the region, linked with fishing effort and the spatiotemporal distribution of the species, will provide a systematic approach to jointly assess and tackle policy challenges. The expansion and improvement of the observer programmes and the advances in electronic monitoring and automated machine learning systems will enable species-specific and high-resolution data of marine megafauna bycatch. Fine-scale GPS tracking of selected species combined with AIS fishery data in concurrent time will allow understanding key drivers of interactions and infer political responsibilities. New fine-scale tracking data of carefully selected species

in the region will allow for a better understanding of their abundance and distribution and post-release mortality. Sightings, fishery catches and tracking data on marine megafauna will be combined to provide a step-changes in predictive habitat mapping approaches to understand overlap and bycatch risk from local to basin-scales. The combination of timeseries of sightings, fishery catches, GPS and GLS tracking and capture-mark-recapture studies, will provide an unprecedented view on megafauna hotspots and the risks and impacts of bycatch, boosting marine spatial planning or pelagic waters. Assessment and testing of mitigation measures will identify key approaches to significantly reduce bycatch in the region. Innovative and efficient data handling, sharing and publishing will establish

an integrated approach to the bycatch data community. Capacity and cooperation between science, fishery industry and policymakers bordering REDUCE will unify stakeholder efforts and utilize an interdisciplinary scientific approach to reduce bycatch of marine megafauna and inform sustainable fishery management in the Eastern Central Atlantic Ocean. The diagnosis and integration of bycatch data across all industrial European fleets in the region, linked with fishing effort and the spatiotemporal distribution of the species, will provide a systematic approach to jointly assess and tackle policy challenges. The expansion and improvement of the observer programmes and the advances in electronic monitoring and automated machine learning systems will enable species-specific and high-resolution data of marine megafauna bycatch. Fine-scale GPS tracking of selected species combined with AIS fishery data in concurrent time will allow understanding key drivers of interactions and infer political responsibilities. New fine-scale tracking data of carefully selected species

in the region will allow for a better understanding of their abundance and distribution and post-release mortality. Sightings, fishery catches and tracking data on marine megafauna will be combined to provide a step-changes in predictive habitat mapping approaches to understand overlap and bycatch risk from local to basin-scales. The combination of timeseries of sightings, fishery catches, GPS and GLS tracking and capture-mark-recapture studies, will provide an unprecedented view on megafauna hotspots and the risks and impacts of bycatch, boosting marine spatial planning or pelagic waters. Assessment and testing of mitigation measures will identify key approaches to significantly reduce bycatch in the region. Innovative and efficient data handling, sharing and publishing will establish

an integrated approach to the bycatch data community. Capacity and cooperation between science, fishery industry and policymakers bordering Eastern Central Atlantic Ocean will be boosted by joint multi-disciplinary workshops, scientific training and monitoring events

A Bycatch Data Scientist position to analyse large data sets and coordinate the datasets on bycatch of the Horizon Europe REDUCE project is offered at University of Barcelona. The Bycatch Data Scientist will analyse large data sets on bycatch, animal tracking, vessel tracking systems and environmental variables, and will ensure that data generated, processed, and shared throughout the project's lifecycle is handled efficiently and in compliance with the program’s guidelines.

The Bycatch Data Scientist will have the following key responsibilities:

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• Data Collection and Integration: Gather data from a variety of sources, particularly bycatch data, GPS tracking, GLS tracking, and vessel tracking systems, ensuring data is comprehensive and relevant to project needs.
• Data Cleaning and Preprocessing: Clean, preprocess, and standardize datasets to ensure data quality, consistency, and usability for analysis.
• Statistical Analysis: Apply statistical techniques to analyse and interpret complex datasets, identifying meaningful patterns, trends, and correlations across multiple data streams.
• Model Development: Develop predictive models to address specific research questions, such as assessing factors involved in bycatch interactions between seabird, vessels and external factors (e.g., fishing activities, environmental variables, etc.).
• Model Validation and Refinement: Continuously validate, test, and refine models to improve their accuracy, reliability, and performance.
• Data Visualization: Create clear, impactful visual representations of data (e.g., charts, graphs, dashboards) to effectively communicate findings to diverse stakeholders.
• Stakeholder Communication: Present findings in a clear, actionable manner to stakeholders, providing insights that inform project decisions and recommendations.
• Innovation in Data Analysis: Experiment with innovative approaches to data analysis, testing new methodologies and exploring advanced techniques for model development.
• Collaboration with Partners: Coordinate data collection efforts among various project partners and stakeholders, ensuring smooth data integration and sharing.
• Data Processing Oversight: Supervise all data processing activities, including cleaning, validation, and transformation, ensuring they adhere to best practices and project protocols.
• Data Documentation: Ensure that all datasets are thoroughly documented with metadata, detailing how the data was collected, processed, and prepared for reuse, in alignment with FAIR principles (Findable, Accessible, Interoperable, Reusable).
• Data Standardization: Enforce data standards to promote ease of use, interoperability, and compatibility between systems, facilitating collaboration between different users.
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• Quality Control: Implement robust data quality control processes to guarantee the accuracy, completeness, and reliability of all datasets.
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• Training and Support: Provide training, guidance, and technical support to project partners, team members and students, whenever necessary.
• Additional Responsibilities: Undertake other tasks as assigned by the Project Coordinator, ensuring alignment with the REDUCE project's goals and objectives.

The application period is from 15 to 28 July 2025.

Gross salary per year: 30.000 €