Additional Information
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Join Siemens Healthineers' Digital Technology & Innovation (DTI) organization as an AI Scientist - Reinforcement Learning & Operational Twinning, where you will develop next-generation AI systems that optimize healthcare operations through intelligent simulation, sequential decision-making, and digital twin technologies.
In this role, you will help expand and operationalize Siemens Healthineers' Operational Twinning research initiatives across complex healthcare environments. Your work will directly influence how hospitals and health systems improve patient flow, resource allocation, scheduling, capacity management, and operational efficiency at scale. Working at the intersection of reinforcement learning, operations research, simulation, and digital twins, you will design intelligent systems capable of modeling and optimizing real-world healthcare workflows within dynamic virtual environments. This role is ideal for candidates passionate about sequential decision systems, AI-driven simulations, and operational optimization challenges with meaningful real-world impact.
You will collaborate closely with cross-functional R&D, engineering, and clinical teams to translate advanced research into scalable production-ready solutions that improve healthcare accessibility, efficiency, and patient outcomes worldwide.
You are responsible for:
Designing and developing reinforcement learning, simulation, and optimization algorithms for Operational Twinning applications in healthcare.
Building intelligent decision-making systems that optimize scheduling, patient flow, triage, staffing, and resource utilization across complex healthcare environments.
Developing AI-driven simulation environments and workflow models capable of representing real-world clinical and operational systems.
Conducting original research in reinforcement learning, sequential decision-making, combinatorial optimization, neural optimization, and hybrid AI-operations research (AI-OR) methods.
Translating large-scale operational and healthcare datasets into actionable optimization and policy-learning solutions.
Rapidly prototyping, evaluating, and validating novel algorithmic approaches for feasibility, scalability, explainability, and operational impact.
Collaborating with multidisciplinary R&D teams to integrate advanced optimization and simulation technologies into Siemens Healthineers' digital health platforms.
Publishing scientific research, contributing to patents, and driving innovation in operational AI and healthcare optimization technologies.
Staying current with advancements in reinforcement learning, world models, AI simulation, operations research, and autonomous decision systems.
Required Qualifications
Ph.D. in Computer Science, Applied Mathematics, Operations Research, Electrical Engineering, Robotics, Artificial Intelligence, or a related technical field.
Strong hands-on experience in reinforcement learning, sequential decision-making systems, or simulation-based optimization.
Experience developing optimization algorithms, operational AI systems, or digital twin/simulation environments.
Strong programming skills in Python and modern ML frameworks such as PyTorch, TensorFlow, or JAX.
Experience translating complex real-world operational problems into scalable AI-driven solutions.
Strong technical communication skills and demonstrated research contributions through publications, patents, or applied research projects.
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