Additive manufacturing (AM) offers key advantages over conventional manufacturing processes, including immense form and design freedom, as well as localized control of material properties. Moreover, the ability to deposit multiple materials and create architected materials further widens the design space. However, fully leveraging these advantages is a complex task, where intuition-based design approaches fall short. To systematically address this challenge, increasing efforts are made to develop AM-oriented computational design approaches. These include techniques such as topology optimization and generative design, where specific aspects of AM are integrated into the computational design process through (efficient) AM process simulations or specification of the fabrication characteristics. The computational demands of these methods have also prompted exploration of machine learning to rapidly predict the process conditions or speed up design iterations. This session aims to provide a forum to exchange ideas and the latest developments regarding computational design techniques for AM. The topic includes contributions focusing on new aspects of: - Topology optimization / generative design in combination with AM design rules. - Development and integration of computationally efficient, physics-based AM process simulations into the computational design process. - Design for spatial distribution of material properties under AM process considerations. - Optimization of support structure layout for AM parts. - Computational design of lattice structures including AM considerations. - Deposition sequence or laser trajectory/tool path optimization. - Recent advances in machine learning and AI for AM-oriented design and process modelling. - Industrial AM case studies and/or identification of new design and simulation challenges.