The Internet of Things (IoT) paradigm proposes massive increases in multimodal data creation on tiny devices located at the far edges of the computing infrastructure. Despite the resource constraints of these devices, communication constraints require the data they produce to be locally processed before transmission. Further, due to the increasing complexity of the data generated, machine learning techniques are being successfully applied at the far edge through so-called TinyML. While most TinyML techniques focus on the individual device, increasingly these devices are both networked together and/or connected to larger cloud or swarm based infrastructures, introducing challenges for managing communication and coordination, both in data collection and usage as well as regarding elements of distributed training and continuous learning. Research into these challenges requires innovation in i) applying novel techniques such as distillation, hardware aware scaling and neural architecture search to implement orchestratable TinyML algorithms, ii) innovative communication, including optimizing the low power communication network connecting edge devices to the infrastructure, and iii) orchestration techniques required for ML-enhanced edge devices to participate in complex distributed applications formed of heterogeneous devices. This interdisciplinary research at the intersection of Artificial Intelligence, Embedded Systems, Distributed Computing, and Low-power Hardware will take into account the candidate’s profile and interests, contributing to the development of innovative solutions for real-world challenges. The candidate will have the opportunity to work with cutting-edge technology, gain valuable experience in interdisciplinary collaboration, and make significant contributions to the field of machine learning at the very edge.