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Supervisor: Prof. Dr. Alexander Tormasov About the Position Constructor Knowledge Labs (CKL), in collaboration with Constructor Technology, invites applications for a PhD position in machine learning for software engineering and formal methods, on the Constructor Fabric project. Constructor Fabric turns a company's informal knowledge into production software through a pipeline of composable capability units called gears : requirements → architecture → product fit → framework (G1) → application → runtime. We are building a World Model over that pipeline — a model that does not just generate code, but predicts the consequences of an architectural decision: total cost of ownership, unintended side effects, latency and failure behaviour, and whether a proposed composition is even admissible. The central object is not a digital twin of the application but its formal architectural skeleton : gear contracts (GearSpec), a typed attributed hypergraph of the application (AppGraph), a composition algebra that defines which assemblies are legal, and several semantic projections (types, protocols, resources, security) over which properties are proved, refuted by counterexample, or reported as an explainable gap . All of it is written in a domain-specific language whose syntax trees and graphs we keep in a projectional editor (JetBrains MPS) — so the DSL is not a convenience layer, it is the thing that defines the model's state space and the boundary of what the model is allowed to propose. What you will work on Depending on your background, one or two of: • Formal skeleton and composition — GearSpec as a contract $(Requires, Provides, State, Effects, Assumptions, Guarantees, Parameters)$; AppGraph as a typed attributed hypergraph; an algebra of assembly (sequential, parallel, feedback, refinement); semantic projections and system-level invariants over trajectories. • Architecture synthesis as an inverse problem — given a specification, load and constraints, find the composition of gears that realises it. Constraint solving and MCTS as the baseline; a learned transition/value model for ranking and pruning; LLM proposals treated strictly as candidate hypotheses that a DSL parser, type checker and property checks must then admit or reject. • DSL design and language engineering — the gear DSL and its glue layer decide which AppGraphs are expressible at all, and therefore where the search space ends. This thread covers the abstract syntax and static semantics of that DSL; the requirement that an architectural decision be unbypassable by construction (you must not be able to wire authentication around the Auth Resolver); what happens to the language when gear contracts change or a 201st gear is added; projectional representation of graphs with cycles; and the open hypothesis that a specialised G1-DSL is generated together with every G1. • Local transition models and pipeline surrogates — fast approximate predictors for single pipeline steps (spec change → code quality, architecture → product fit, app → runtime outcome), with calibrated uncertainty, validated against real telemetry. • Data — reconstructing (specification → architecture) pairs from open repositories at scale, plus synthetic project generation from a fixed gear set and DSL grammar to cover rare transitions, gaps and counterexamples. Profile • MSc (or equivalent) in computer science, mathematics, or a mathematical natural science. • Coursework in compilers, programming-language theory, language design or model-driven engineering is a strong positive signal, half of this project is deciding what a formal architecture description is allowed to say. • Evidence that you finish things: a maintained op

PhD Position: World Models for Software Architecture Synthesis (Constructor Fabric)

Don't apply unprepared