a project to join

HOLISUN is a software research and development company with 22 years of experience, specializing in Artificial Intelligence with a strong focus on smart agriculture, environmental monitoring, and sustainability. We have a strong track record of implementing numerous EU-funded projects, including CHIST-ERA, ERA-NET, and Horizon 2020.

Our core technical expertise spans across Artificial Intelligence, Machine Learning / Deep Learning, Combinatorial Optimizations, Cloud Computing, Big Data / Data Mining, Augmented Reality, and Predictive Analysis. Within these domains, we heavily utilize advanced methodologies, including Hybrid Models, Model Ensembles, and Data Drift adaptation.

We excel in developing IoT-based predictive analytics, creating Data Quality Assessment Methodologies (DQAM), and defining Data Sustainability metrics for complex sensor networks. Our current R&D efforts actively integrate Digital Twin technologies for precise ecosystem modeling, synthetic data generation, and land bonitation.

Building on our extensive European project experience, we deliver robust edge-fog data augmentation architectures, vulnerable-by-design IoT testbeds, and privacy assessment frameworks. Our team seamlessly bridges the gap between high-level academic research (frequently publishing in Q1 journals) and practical, market-ready software solutions for environmental and agricultural optimization.

Topic 1. Integrated Monitoring and Assessment for Sustainable Water Management
Topic 2. Nature-Based Approaches for Resilient Water Management at Hydrological and Hydrogeological System Scale
Topic 3. Smart, Secure and Adaptive Water Infrastructures for Sustainable Water Management
Topic 4. Resilient Water Systems Through Systemic Change, Policy Coherence and Inclusive Governance

We are seeking to join a consortium as a dedicated technical and R&D partner, providing advanced AI, machine learning, and software engineering solutions. We bring deep expertise in processing complex environmental data, building predictive models, and deploying robust IoT architectures.
We are highly interested in projects that aim to develop Digital Twins of natural habitats, monitor climate or biodiversity changes through smart sensor networks, or require sophisticated data sustainability frameworks. Our contribution would center on handling the full data lifecycle: from edge-fog smart data augmentation and ensuring data quality/privacy, to the deployment of advanced AI algorithms (including metaheuristics and hybrid models) that turn raw environmental data into actionable insights for conservation and sustainability.

artificial intelligence, machine learning, digital twins, iot sensors, predictive analytics, ecosystem modeling, data sustainability, data drift, environmental monitoring.

--