The recruitment and early community assembly of deep-sea corals and sponges remain poorly understood, particularly in deep-sea regions. This project uses Autonomous Reef Monitoring Structures (ARMS) to investigate metazoan diversity and early-stage recruitment in deep-sea coral gardens. The ARMS were placed inside, outside and on the periphery of a coral garden, consisting of the octocoral Primnoa pacifica. ARMS provide standardized, repeatable sampling of sessile and mobile invertebrates, allowing the detection of cryptic and early life stages that are otherwise difficult to observe. Using high-throughput metabarcoding of multiple genetic markers (COI and mutS), we will characterize the composition, diversity, and successional patterns of the metazoan communities settling on ARMS over a 1 year deployment period. This study will provide insights into the processes driving community assembly in deep-sea ecosystems. Analysis is carried out in a MSc project (60 credits). The project relies on computationally intensive processing of large metabarcoding datasets, including quality filtering, sequence clustering, taxonomic assignment, and diversity analyses. Access to high-performance computing resources is essential to efficiently handle these large datasets, perform reproducible bioinformatic pipelines, and enable the exploration of ecological patterns at fine taxonomic resolution. The outcomes will improve our understanding of deep-sea biodiversity, inform conservation and management of vulnerable coral habitats, and establish workflows for the efficient processing of large-scale metabarcoding datasets. By linking recruitment patterns to environmental variables, this project contributes critical knowledge to support the resilience and management of deep-sea ecosystems.