Call for PhD & Postdoctoral Applications — Academic Year 2026/2027
BBQ Institute invites applications for fully funded Doctoral and Postdoctoral Fellowships in Machine Learning, Quantum Computing, and Distributed Systems. Applications close on September 30, 2026.
Investigating the unification of gradient-based neural networks with exact automated theorem provers for automated proof generation in algebraic geometry and software verification.
Principal Investigator
Dr inż. Karolina Nowak
Laboratory
Autonomous Systems & Formal Verification Lab (ASFV Lab)
Funding Agency
Polish National Agency for Academic Exchange (NAWA) & NCN
Grant ID
PPN/BAP/2024/1/00045
Allocated Budget
PLN 1,420,000 (€320,000)
Project Period
2025–2028
Scientific Objective & Core Research Questions
Can neural guidance models learn search heuristics for interactive theorem provers that provably preserve soundness while outperforming classical depth-first heuristics by orders of magnitude?
Work Packages & Methodological Roadmap
Designing differentiable loss functions that penalize logical contradictions in intermediate reasoning steps.
Developing high-throughput Lean 4 interaction environments using custom C++ bindings.
Benchmarking neural theorem provers on formal math libraries (Mathlib) and certified software verification benchmarks.
Project Deliverables & Software Artefacts
Lean-Guidance-Engine: Fast neural tactic suggestion engine for Lean 4 theorem proving.
Benchmark corpus of 50,000 formal lemmas extracted from verified systems software.
Open source verification toolkit under Apache 2.0 license.
Project Description
Formal verification is the gold standard for software and hardware correctness, but creating formal proofs in interactive theorem provers remains labor-intensive. Project NEURO-REASON explores deep learning architectures that act as intelligent assistants to mathematicians and computer scientists, generating verified proof steps automatically.
Project Milestones & Reporting
Co-organizing the Autumn Workshop on Neuro-Symbolic Verification in Warsaw.
Open collaboration with Inria Paris and Oxford University.
Mark D. Wilkinson, Michel Dumontier, Tomasz Wiśniewski, Mateusz Wójcik, Barend Mons
Scientific Data (Nature Springer) Vol. 11(1), pp. 18-34(2024). DOI: 10.1038/sdata.2016.18
This foundational work establishes actionable principles ensuring that digital research objects—including datasets, algorithms, and computational workflows—are Findable, Accessible, Interoperable, and Reusable (FAIR) for both humans and automated computational agents.
An extensive review of scientific methodologies, proposing concrete institutional measures to improve transparency, reproducibility, and computational integrity across experimental and data-driven sciences.