The 22nd IEEE International eScience Conference (eScience’26) will be held in Naples from September 28 to October 2, 2026.

eScience studies, enacts, and improves the ongoing process of innovation in computationally intensive or data-intensive research methods; typically, this is carried out collaboratively, often using distributed computing infrastructure. eScience encompasses all fields of research and addresses all stages of the research lifecycle, from formulation of the research questions, through large-scale simulations and data analytics, scientific discovery, up to long-term sharing, publication, reusing, and reapplying of the results, data, as well as the relevant tools, processes, and knowledge.

eScience’26 welcomes paper submissions for its technical program. The conference will be an interdisciplinary forum for researchers, developers, and users of applications and enabling IT technologies. The conference welcomes conceptualization, implementation, and experience contributions that will allow and drive innovation in data- and compute-intensive research. eScience covers all disciplines, from the physical and biological sciences to the social sciences, arts, and humanities. Infrastructure and technologies can span a broad spectrum, including HPC, cloud, IoT, and artificial intelligence and machine learning methods.

Submissions may include all aspects of eScience and its associated technologies, applications, algorithms, and tools, with a strong focus on practical solutions and open challenges. Ideal papers involve the interplay between applications and infrastructure technologies, with an emphasis on novelty in one or both.

Topics of interest related to eScience also include, but are not limited to:

    Artificial intelligence, machine learning, data science, generative AI, and large language models are/applicable in science
    Research computing cyberinfrastructure (e.g., cloud, cluster, HPC, supercomputer)
    Findable, Accessible, Interoperable, Reusable (FAIR) principles for scientific software, data, workflows, and models
    Reproducible and replicable eScience
    Translational research in computer and computational sciences
    Continuum computing: convergence between cloud computing, edge computing, and/or the Internet of Things (IoT)
    Education and e-Science
    Collaborative, reproducible, and replicable eScience
    Science gateways, data portals, and digital repositories
    Resource management and scheduling
    Programming paradigms and models
    Real-time (time-sensitive) computing (e.g., for scientific instruments)
    Automation and event-based computing
    Big data stacks and significant data ecosystems
    File and storage systems, I/O, and data management
    Scientific applications, algorithms, tools, and technologies
    Scientific workflows and distributed computing paradigms (e.g., FaaS)
    Blockchain technologies in science
    Fault tolerance, resilience, and security