Mobius - AI Research Scientist

Description

• 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 :
• Meta-learning, recursive architectures, and AI safety.
• Distributed systems, multi-agent environments, and decentralized coordination.
• 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.