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Postdoctoral In Reinforcement Learning Jobs in Missouri

Jobgether

$76K - $96K/yr

Research and apply advanced methods in safe reinforcement learning, constrained control, scenario planning, Bayesian reinforcement learning, and related areas to ensure reliable and secure AI agent ...

Jobgether

$80K - $110K/yr

Deep expertise in at least one relevant area, including: * Reinforcement learning. * Imitation learning. * Multimodal generative modeling. * Computer vision. * Robotics. * Planning and control ...

New

Washington University of St. Louis

The postdoc will also have the opportunity to work on other topics related to air quality modeling ... Strong programming skills and experience with machine learning applications in geoscience are ...

Washington University of St. Louis

David Spencer's Lab in the Division of Oncology, Department of Medicine. Dr. David Spencer is ... deep learning methods. There will be opportunities for development of new cell line and animal ...

Walmart

Principal, Data Scientist (Pricing)

Anderson, MO · On-site

$110K - $220K/yr

... learning, reinforcement learning), causal inference, experimentation, and statistics to solve ... Extensive experience (8+ years) in data science, machine learning, and statistical analysis, with a ...

New

Walmart

Principal, Data Scientist (Pricing)

Noel, MO · On-site

$110K - $220K/yr

... learning, reinforcement learning), causal inference, experimentation, and statistics to solve ... Extensive experience (8+ years) in data science, machine learning, and statistical analysis, with a ...

New

Walmart

Principal, Data Scientist (Pricing)

Cassville, MO · On-site

$110K - $220K/yr

... learning, reinforcement learning), causal inference, experimentation, and statistics to solve ... Extensive experience (8+ years) in data science, machine learning, and statistical analysis, with a ...

New

Jobgether

$80K - $110K/yr

Requirements: The ideal candidate brings deep expertise in machine learning, AI systems, and ... Knowledge of reinforcement learning, preference learning, reward modeling, or teacher-student ...

New

Washington University of St. Louis

Information on being a postdoc at Washington University in St. Louis can be found at Lab website ... Biomarker identification through the use of machine learning and AI approaches. * Integration of ...

Walmart

Staff, Data Scientist (Pricing)

Anderson, MO · On-site

$110K - $220K/yr

Optimization & Reinforcement Learning: Multi-armed bandits, Deep RL (PPO, DQN) for sequential ... Drive best practices in AgentOps: Build Agentic workflows to enable chat-based price explainability ...

New

Walmart

Staff, Data Scientist (Pricing)

Cassville, MO · On-site

$110K - $220K/yr

Optimization & Reinforcement Learning: Multi-armed bandits, Deep RL (PPO, DQN) for sequential ... Drive best practices in AgentOps: Build Agentic workflows to enable chat-based price explainability ...

New

Walmart

Staff, Data Scientist (Pricing)

Noel, MO · On-site

$110K - $220K/yr

Optimization & Reinforcement Learning: Multi-armed bandits, Deep RL (PPO, DQN) for sequential ... Drive best practices in AgentOps: Build Agentic workflows to enable chat-based price explainability ...

New

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Postdoctoral In Reinforcement Learning information

What is the difference between Postdoctoral In Reinforcement Learning vs Postdoctoral In Machine Learning?

AspectPostdoctoral In Reinforcement LearningPostdoctoral In Machine Learning
Required CredentialsPhD in Computer Science, AI, or related field; strong programming skills; research experience in reinforcement learningPhD in Computer Science, AI, or related field; strong programming skills; research experience in machine learning
Work EnvironmentAcademic labs, research institutions, industry R&D teams focused on reinforcement learning applicationsAcademic labs, research institutions, industry R&D teams working on various machine learning techniques
Industry UsagePrimarily in AI research, robotics, gaming, and autonomous systemsBroader applications including data analysis, predictive modeling, and AI research

Postdoctoral In Reinforcement Learning specializes in research related to decision-making algorithms and autonomous systems, whereas Postdoctoral In Machine Learning covers a wider range of AI techniques. Both roles require similar credentials but differ in focus and application areas.

What are the key skills and qualifications needed to thrive as a postdoctoral researcher in reinforcement learning?

To thrive as a Postdoctoral Researcher in Reinforcement Learning, you need a PhD in computer science or a related field, with deep expertise in machine learning, statistics, and algorithm development. Proficiency in programming languages such as Python, experience with deep learning frameworks (e.g., TensorFlow or PyTorch), and familiarity with reinforcement learning libraries are typically required. Strong analytical thinking, problem-solving ability, collaboration, and scientific communication skills help you excel in research teams and publish impactful work. These competencies are vital to advancing state-of-the-art research, developing novel algorithms, and contributing to the academic and industrial progress in AI.

What are some common challenges faced by postdoctoral researchers in reinforcement learning, and how can they be addressed?

Postdoctoral researchers in reinforcement learning often face challenges such as balancing independent research projects with collaborative work, staying up-to-date with rapidly evolving literature, and managing the pressure to publish in top conferences. Effective time management, regular engagement with the research community through seminars and workshops, and seeking mentorship from senior colleagues can help address these challenges. Additionally, collaborating with interdisciplinary teams can offer fresh perspectives and support, making it easier to navigate complex research problems.

What is a postdoctoral researcher in reinforcement learning?

A Postdoctoral Researcher in Reinforcement Learning is an individual who has completed a PhD and conducts advanced research in the field of reinforcement learning, a branch of artificial intelligence focused on how agents take actions in environments to maximize rewards. These researchers often work in academic, industrial, or governmental research settings, collaborating on projects that advance the theoretical foundations or practical applications of reinforcement learning. Their responsibilities may include designing experiments, developing algorithms, publishing papers, and mentoring graduate students.
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What job categories do people searching Postdoctoral In Reinforcement Learning jobs in Missouri look for? The top searched job categories for Postdoctoral In Reinforcement Learning jobs in Missouri are:
What cities in Missouri are hiring for Postdoctoral In Reinforcement Learning jobs? Cities in Missouri with the most Postdoctoral In Reinforcement Learning job openings:
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Infographic showing various Postdoctoral In Reinforcement Learning job openings in Missouri as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Senior AI Research Scientist (Model-based RL)

Jobgether

On-site, Remote

$76K - $96K/yr

Full-time

Medical, Dental, Vision, PTO

Posted 9 days ago

Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior AI Research Scientist (Model-based RL) based in Netherlands.

This role offers the opportunity to shape the future of intelligent industrial automation through advanced artificial intelligence research.
You will develop cutting-edge reinforcement learning systems that enable real-world machines and facilities to continuously learn and optimize performance.
Working at the intersection of AI research, control theory, and industrial applications, you will help transform complex operational environments.
The position combines deep technical exploration with practical deployment, turning innovative research into impactful solutions.
You will collaborate with multidisciplinary experts, contribute to ambitious research initiatives, and influence the direction of AI-driven control systems.
This is an ideal opportunity for a researcher passionate about applying advanced AI techniques to solve large-scale, real-world challenges.

Accountabilities:
  • Design, implement, and evaluate model-based reinforcement learning agents, including planning-based controllers such as MPC and MPPI, as well as the software prototypes required for deployment in real industrial control systems.
  • Develop learned dynamics models and world models capable of generalizing across different systems, including training approaches such as pretraining, curriculum learning, active learning, adversarial learning, and fine-tuning.
  • Research and apply advanced methods in safe reinforcement learning, constrained control, scenario planning, Bayesian reinforcement learning, and related areas to ensure reliable and secure AI agent deployment.
  • Translate research discoveries into practical outcomes by developing production-ready solutions and leading the rollout of research initiatives or large-scale projects.
  • Communicate research findings, technical developments, and project results clearly through written documentation, presentations, and internal or external discussions.
  • Collaborate with research teams, engineers, and external partners to transform innovative AI concepts into impactful industrial applications.
  • Mentor and guide Research Engineers by helping them apply advanced AI research methodologies to complex industrial challenges.
  • Independently define new research directions and contribute to the long-term evolution of intelligent control technologies.
Requirements:
  • PhD in machine learning, control systems, computer science, or a related technical field, or equivalent practical experience with strong expertise in model-based reinforcement learning.
  • At least 2 years of research experience in academia or industry after completing a PhD.
  • Deep knowledge and hands-on experience in areas such as model-based reinforcement learning, model-free reinforcement learning, safe reinforcement learning, planning algorithms, world models, learned dynamics models, deep learning, or control theory.
  • Proven experience building and evaluating AI agents using simulators, including experience addressing the challenges of simulation-to-real-world transfer.
  • Strong programming skills in Python and experience with machine learning frameworks such as PyTorch and scientific computing libraries such as SciPy.
  • Experience working with scalable experimentation environments and infrastructure such as distributed computing, Ray, Kubernetes, Docker, or cloud platforms like GCP.
  • Strong research background demonstrated through publications or contributions in reinforcement learning, control systems, artificial intelligence, or related fields.
  • Ability to collaborate effectively in a remote, international environment while demonstrating ownership, transparency, empathy, operational excellence, and strong teamwork.
  • Passion for applying AI research to industrial systems and improving efficiency, sustainability, and resource utilization.
Benefits:
  • Competitive base salary ranging from 87,681 to 165,379, depending on location tier, experience, qualifications, and other relevant factors.
  • Eligibility for meaningful equity participation.
  • Fully remote work environment with flexibility across multiple time zones.
  • Medical, dental, and vision insurance, with benefits varying depending on location.
  • Unlimited paid time off with a required minimum of 20 days per year.
  • Paid parental leave, depending on regional policies.
  • Flexible stipends supporting workspace setup, personal well-being, and professional development.
  • Company-provided MacBook.
  • Opportunities for significant ownership, career growth, and professional development in a fast-paced AI-focused environment.
  • Access to training programs covering technical skills, product knowledge, customer immersion, and professional growth.
  • Remote-first culture built around documentation, asynchronous collaboration, regular team communication, and virtual team-building activities.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
 Why Apply Through Jobgether? 
 
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
 
 
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We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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