Mobius - AI Research Scientist
3 days ago
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.
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