DSTrain · Data Science Postdoctoral Training Programme

DSTrain - Data Science Postdoctoral Training Programme

Data science is a rapidly growing field playing a central role in modern science and innovation. Increasing volumes of data create major opportunities across academia, industry, and the public sector. Thus, there is strong demand for researchers with deep data-science expertise. The green and digital transitions require advanced data-driven solutions in areas such as climate science, energy systems, health, and industrial innovation. However, there is a shortage of researchers who combine computational expertise with strong disciplinary knowledge and cross-sector experience. DSTrain addresses this by establishing an interdisciplinary, internationally competitive postdoctoral programme in data science, natural sciences and technology, training 36 fellows over 5 years, and responds to four key needs: 1-Interdisciplinary integration: DSTrain embeds fellows in interdisciplinary research environments with joint supervision across disciplines. 2-Advanced digital skills: The programme develops expertise in machine learning, AI, high-performance computing, UQ, and FAIR data through research training and transferable skills development. 3-Academia-industry collaboration: Collaboration with 29 associated partners strengthen knowledge exchange, mobility, and innovation. 4- Improved research careers and HR practices: DSTrain follows open, transparent recruitment and supports diversity, gender equality, mentoring, and structured career development in line with European Research Area principles. Objectives: • Enable excellent interdisciplinary research at the interface of data science and domain sciences. • Train highly employable researchers with methodological, interdisciplinary and transferable competences. • Strengthen international and intersectoral mobility. • Contribute to institutional alignment with ERA priorities, open science, and responsible research. Immediate impact: • 36 postdoctoral fellows. • High-quality publications, datasets, and tools shared under FAIR and OA principles. • Strengthened collaboration between academia, research institutes, industry and public actors. Short- to medium-term impact: • Fellows placed in competitive positions across sectors. • Durable cross-sector networks. • Enhanced European expertise in AI, computational sustainability, and digital technologies. • Strengthened institutional practices in research data management and responsible AI. System-level impact: DSTrain contributes to Europe’s digital transformation, green transition, and research excellence by building advanced human capital and embedding interdisciplinary, intersectoral, and open science practices in institutional structures. Long-term societal impact: The programme strengthens Europe’s capacity to address climate change, energy transition, sustainable industry, and evidence-based public policy through robust, explainable, and ethically grounded data-driven research. Secondments: The secondments provide structured opportunities to apply data science methods in operational settings, strengthening the postdoctoral fellows’ ability to translate analytical developments into application across disciplines and sectors. Secondments are tailored to the fellows’ research projects, expertise, and interests, and matched with suitable partners through a structured, dialogue-based selection process. This enables: • Short placements aligned with project timelines and partner availability. • Application of models, algorithms, and analytical workflows within partner-specific data. • Exposure to sector-specific regulatory, technical, and organisational contexts. • Reciprocal exchange, through which partners benefit from advanced analytical expertise while providing practical and domain-specific perspectives. The secondments contribute to strengthening the translational capacity of data science within and beyond academia. The programme supports the formation of researchers capable of applying advanced data science methods across disciplinary and sectoral boundaries, thereby contributing to broader digital and innovation objectives.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз