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Melbourne
1 week ago
Research Scientist – AI Enabled Decision-Making logo

Research Scientist – AI Enabled Decision-Making

Praetorian Aeronautics

Melbourne
1 week ago
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Research Scientist – AI Enabled Decision-Making

Praetorian Aeronautics is seeking a Research Scientist in AI-enabled decision-making to design and validate algorithms for autonomous and semi-autonomous systems. The role involves formulating decision-making problems as MDPs/POMDPs, applying reinforcement learning, Monte Carlo tree search, and combinatorial optimisation, and prototyping in Python. Candidates must be eligible to work in Australia and hold citizenship of Australia or another Five Eyes nation.

Core AIRemoteFull-timeEntry LevelPythonReinforcement Learning

Research Scientist – AI Enabled Decision-Making

Praetorian Aeronautics is seeking a Research Scientist in AI-enabled decision-making to design and validate algorithms for autonomous and semi-autonomous systems. The role involves formulating decision-making problems as MDPs/POMDPs, applying reinforcement learning, Monte Carlo tree search, and combinatorial optimisation, and prototyping in Python. Candidates must be eligible to work in Australia and hold citizenship of Australia or another Five Eyes nation.

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Core AIRemoteFull-timeEntry LevelPython

Salary

Not specified

Work Location

Melbourne, Victoria, Australia, AU

Work Model

Remote

Employment Type

Full-time

Experience Level

Not Applicable

Core Qualifications

Technical (Must-have)
PythonReinforcement LearningMonte Carlo Tree SearchValue IterationPolicy IterationCombinatorial OptimisationLinear ProgrammingInteger ProgrammingGenetic AlgorithmsGreedy HeuristicsScikit-LearnPyTorchMDPsPOMDPsMachine Learning

Preferred Qualifications

Technical (Nice-to-have)
C++RustLLMsPath Planning AlgorithmsMulti-Agent Decision-MakingPartially Observable Decision-Making

Key Responsibilities

  • •Formulate and solve sequential decision-making problems as MDPs or POMDPs using RL, MCTS, and value/policy iteration
  • •Design combinatorial optimisation approaches (LP/IP, genetic algorithms, greedy heuristics) for single- and multi-objective resourcing and allocation problems
  • •Prototype and validate algorithms in Python using standard ML/DL tooling (Scikit-Learn, PyTorch, etc.)
  • •Extend approaches into partially observable and multi-agent settings where relevant
  • •Work with flight sciences and engineering teams to translate research into deployable decision-making capability
  • •Contribute to the team's publication record where opportunities align with commercial priorities
Research ScientistAIDecision-MakingDefenseAutonomous SystemsReinforcement LearningCombinatorial OptimisationPythonPhDRemote
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