Graph Neural Networks (GNNs) work directly with graph-structured data, capitalising on relational information among entities. One limitation of GNNs is their reliance on local interactions among connected nodes. GNNs may generate identical node embeddings for similar local neighbourhoods and fail to distinguish structurally distinct graphs. Positional encodings help to break the locality constraint by informing the nodes of their global positions in the graph. Furthermore, they are required by Graph Transformers to encode structural information. However, existing positional encodings based on the graph Laplacian only encode structural information and are typically fixed. To address these limitations, we propose a novel approach to design positional encodings using sheaf theory. The sheaf Laplacian can be learnt from node data, allowing it to encode both the structure and semantic information. We present two methodologies for creating sheaf-based positional encodings, showcasing their efficacy in node and graph tasks. Our work advances the integration of sheaves in graph learning, paving the way for innovative GNN techniques that draw inspiration from geometry and topology.