PhD Position (m/f/d): World Models for Software Architecture Synthesis (Constructor Fabric)
Core
PhD research on building a World Model for software architecture synthesis, predicting consequences of architectural decisions using formal methods and machine learning.
Role type
PhD researcher (Machine Learning for Software Engineering)
Builds
A formal architectural skeleton (GearSpec, AppGraph) and a World Model that predicts system properties and validates architectural compositions.
Domain
Software Engineering, Formal Methods, Machine Learning, Model-Driven Engineering
Deliverable
production ML models
Required skills
Probability and statistics, Linear algebra, Discrete mathematics and graph theory, Mathematical logic and formal methods, Sequential decision making
Preferred skills
Formal languages and language engineering, Category theory, Causal inference, Queueing theory, Combinatorial optimisation, Representation learning theory
Technologies
Python, PyTorch Geometric, DGL, Z3, cvc5, TLA+, Lean, Coq, tree-sitter, Ray, Dask, W&B, MLflow, Hydra, JetBrains MPS
Responsibilities
Designing formal skeletons and composition algebras, solving architecture synthesis as an inverse problem, designing DSLs and language engineering, building local transition models and pipeline surrogates, reconstructing and generating data for training.
Seniority
PhD Candidate
