2 Agentbased Simulators Jobs

The job involves designing architectures for meta-learning, self-reflective agents, and recursive optimization loops. Building simulation frameworks grounded in Bayesian dynamics, attractor theory, and teleo-dynamics. Developing systems that integrate graph rewriting, knowledge representation, and neurosymbolic reasoning. Researching fractal intelligence structures, swarm-based agent coordination, and autopoietic systems. Advancing Mobius's knowledge graph with ontologies supporting logic, agency, and emergent semantics. Integrating logic into distributed decision graphs aligned with business and ethical constraints. Publishing cutting-edge results and mentoring contributors in reflective system design and emergent AI theory. Building scalable simulations of multi-agent ecosystems within the Mobius runtime. You should have a Ph.D. or M.Tech in Artificial Intelligence, Cognitive Science, Complex Systems, Applied Mathematics, or equivalent experience. Proven expertise in meta-learning, recursive architectures, and AI safety. Strong knowledge of distributed systems, multi-agent environments, and decentralized coordination. Proficiency in formal and theoretical foundations like Bayesian modeling, graph theory, and logical inference. Strong implementation skills in Python, additional proficiency in C++, functional or symbolic languages are a plus. A publication record in areas intersecting AI research, complexity science, and/or emergent systems is required. Preferred qualifications include experience with neurosymbolic architectures, hybrid AI systems, fractal modeling, attractor theory, complex adaptive dynamics, topos theory, category theory, logic-based semantics, knowledge ontologies, OWL/RDF, semantic reasoners, autopoiesis, teleo-dynamics, biologically inspired system design, swarm intelligence, self-organizing behavior, emergent coordination, distributed learning systems like Ray, Spark, MPI, or agent-based simulators. Technical proficiency required in Python, preferred in C++, Haskell, Lisp, or Prolog for symbolic reasoning. Familiarity with frameworks like PyTorch, TensorFlow, distributed systems like Ray, Apache Spark, Dask, Kubernetes, knowledge technologies including Neo4j, RDF, OWL, SPARQL, experiment management tools such as MLflow, Weights & Biases, GPU and HPC systems like CUDA, NCCL, Slurm, and formal modeling tools like Z3, TLA+, Coq, Isabelle. Core research domains include recursive self-improvement and introspective AI, graph theory, graph rewriting, knowledge graphs, neurosymbolic systems, ontological reasoning, fractal intelligence, dynamic attractor-based learning, Bayesian reasoning, cognitive dynamics, swarm intelligence, decentralized consensus modeling, topos theory, autopoietic system architectures, teleo-dynamics, and goal-driven adaptation in complex systems.,

You will be responsible for designing architectures for meta-learning, self-reflective agents, and recursive optimization loops. Your role will involve building simulation frameworks for behavior grounded in Bayesian dynamics, attractor theory, and teleo-dynamics. Additionally, you will develop systems that integrate graph rewriting, knowledge representation, and neurosymbolic reasoning. Conducting research on fractal intelligence structures, swarm-based agent coordination, and autopoietic systems will be part of your responsibilities. You are expected to advance Mobius's knowledge graph with ontologies supporting logic, agency, and emergent semantics. Integration of logic into distributed, policy-scoped decision graphs aligned with business and ethical constraints is crucial. Furthermore, publishing cutting-edge results and mentoring contributors in reflective system design and emergent AI theory will be part of your duties. Lastly, building scalable simulations of multi-agent, goal-directed, and adaptive ecosystems within the Mobius runtime is an essential aspect of the role. In terms of qualifications, you should have proven expertise in meta-learning, recursive architectures, and AI safety. Proficiency in distributed systems, multi-agent environments, and decentralized coordination is necessary. Strong implementation skills in Python are required, with additional proficiency in C++, functional, or symbolic languages being a plus. A publication record in areas intersecting AI research, complexity science, and/or emergent systems is also desired. Preferred qualifications include experience with neurosymbolic architectures and hybrid AI systems, fractal modeling, attractor theory, complex adaptive dynamics, topos theory, category theory, logic-based semantics, knowledge ontologies, OWL/RDF, semantic reasoners, autopoiesis, teleo-dynamics, biologically inspired system design, swarm intelligence, self-organizing behavior, emergent coordination, and distributed learning systems. In terms of technical proficiency, you should be proficient in programming languages such as Python (required), C++, Haskell, Lisp, or Prolog (preferred for symbolic reasoning), frameworks like PyTorch and TensorFlow, distributed systems including Ray, Apache Spark, Dask, Kubernetes, knowledge technologies like Neo4j, RDF, OWL, SPARQL, experiment management tools like MLflow, Weights & Biases, and GPU and HPC systems like CUDA, NCCL, Slurm. Familiarity with formal modeling tools like Z3, TLA+, Coq, Isabelle is also beneficial. Your core research domains will include 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, top os theory, and the abstract structure of logic spaces, autopoietic, self-sustaining system architectures, and teleo-dynamics and goal-driven adaptation in complex systems.,