Description :

Key Responsibilities :

- Design architectures for meta-learning, self-reflective agents, and recursive optimization loops.

- Build simulation frameworks for I behavior grounded in Bayesian dynamics, attractor theory, and teleo-dynamics.

- Develop systems that integrate graph rewriting, knowledge representation, and neurosymbolic reasoning.

- Conduct research on fractal intelligence structures, swarm-based agent coordination, and autopoietic systems.

- Advance Mobiuss knowledge graph with ontologies supporting logic, agency, and emergent semantics.

- Integrate I logic into distributed, policy-scoped decision graphs aligned with business and ethical constraints.

- Publish cutting-edge results and mentor contributors in reflective system design and emergent AI theory.

- Build scalable simulations of multi-agent, goal-directed, and adaptive ecosystems within the Mobius runtime.

Required Qualifications :

- Proven expertise in :

1. Meta-learning, recursive architectures, and AI safety.

2. Distributed systems, multi-agent environments, and decentralized coordination.

3. Formal and theoretical foundations, including Bayesian modeling, graph theory, and logical inference.

- Strong implementation skills in Python (required), with additional proficiency in C++, functional or symbolic languages being a plus.

- Publication record in areas intersecting AI research, complexity science, and/or emergent systems.

Preferred Qualifications :

- Experience with : Neurosymbolic architectures and hybrid AI systems.

- Fractal modeling, attractor theory, and complex adaptive dynamics.

- Topos theory, category theory, and logic-based semantics.

- Knowledge ontologies, OWL/RDF, and semantic reasoners.

- Autopoiesis, teleo-dynamics, and biologically inspired system design.

- Swarm intelligence, self-organizing behavior, and emergent coordination.

- Distributed learning systems : Ray, Spark, MPI, or agent-based simulators.

Technical Proficiency :

- Programming Languages : Python (required), C++, Haskell, Lisp, or Prolog (preferred for symbolic reasoning.

- Frameworks : PyTorch, TensorFlow.

- Distributed Systems : Ray, Apache Spark, Dask, Kubernetes.

- Knowledge Technologies : Neo4j, RDF, OWL, SPARQL.

- Experiment Management : MLflow, Weights & Biases.

- GPU and HPC Systems : CUDA, NCCL, Slurm.

- Formal Modeling Tools : Z3, TLA+, Coq, Isabelle.

Core Research Domains :

- Recursive self-improvement and introspective AI.

- Graph theory, graph rewriting, and knowledge graphs.

- Neurosymbolic systems and ontological reasoning.

- Fractal intelligence and dynamic attractor-based learning.

- Bayesian reasoning under uncertainty and cognitive dynamics.

- Swarm intelligence and decentralized consensus modeling.

- Topos theory and abstract structure of logic spaces.

- Autopoietic, self-sustaining system architectures.

- Teleo-dynamics and goal-driven adaptation in complex systems.