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data. Manage preferences for further information and to change your choices. Accept all cookies Reject optional cookies Skip to main content Postdoc Position in Neuro-morphic Reinforcement Learning
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Doctoral Researchers in Reinforcement Learning Employer AALTO UNIVERSITY Location Finland (FI) Salary 3143 €/month Closing date 23 Oct 2026 View more categories View less categories Job Type Research
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Max Planck ETH Center for Learning Systems | New Germany, KwaZulu-Natal | South Africa | 18 days ago
, Natural Language Processing, Neuroinformatics, Optimization, Physical AI, Probabilistic Models, Reinforcement Learning, Robotics, Security and Privacy, Smart Materials, Social Questions, Soft Robotics
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Your Job Develop a reinforcement learning (RL) controller for a liquid–liquid gravity settler, trained entirely offline in a simulated environment Use existing physics-informed neural network (PINN
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-funded project focused on continual reinforcement learning for real-world systems. The grant provides a stable, well-resourced four-year research environment to tackle a problem that classical RL
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systems, such as transformer or diffusion networks and reinforcement learning to guide the self-aware learning and network formation. As such this expected to be a purely mathematical and
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systems, such as transformer or diffusion networks and reinforcement learning to guide the self-aware learning and network formation. As such this expected to be a purely mathematical and
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under adversarial and disrupted conditions. The research will investigate agentic AI and reinforcement learning-based control for adaptive spectrum usage, power control, scheduling, and multi-RAT
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Computational Fluid Dynamics (CFD), fluid mechanics, and Artificial Intelligence (AI), with a particular focus on developing deep reinforcement learning methods for active flow control of hydraulic
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and applying optimisation methods, such as model predictive control, multi-objective optimisation and, where appropriate, reinforcement learning Investigating suitable model-reduction and approximation