Research Internship / Work-Study

neoBIM is a well-funded start-up software company revolutionizing the way architects design buildings with our innovative BIM (Building Information Modelling) software. As we continue to grow, we are building a small and talented team of developers to drive our software forward. This internship focuses on the application of Generative Flow Networks (GFlowNets) to architectural design and spatial generation problems. The role involves developing generative AI models that can sample diverse, constraint-aware architectural solutions, supporting early-stage design exploration and research-driven prototyping. The position is suitable for Master’s-level students interested in the intersection of architecture, AI, and computational design, and may be aligned with academic research or thesis work. Tasks Implement GFlowNet-based generative models for architectural design tasks • Represent architectural layouts, massing, or spatial graphs as sequential decision processes • Design and test reward functions encoding architectural constraints and objectives • Generate and evaluate multiple architectural design alternatives • Analyze results in terms of diversity, feasibility, and performance • Compare GFlowNet approaches with baseline generative or reinforcement learning methods • Contribute to documentation, experiments, and research deliverables Requirements Required Skills • Experience with Python and PyTorch • Familiarity with machine learning and reinforcement learning concepts • Interest in computational architecture or generative design • Ability to work independently on research-driven tasks • Strong analytical and documentation skills Technical Stack Machine Learning • Python • PyTorch • NumPy Generative Modeling • GFlowNet implementations • Reinforcement learning fundamentals Computational Design • Graph libraries (NetworkX, PyTorch Geometric) • Geometry processing tools (shapely, trimesh) • Optional: Rhino / Grasshopper Developm