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Postdoctoral In Reinforcement Learning Jobs in New Mexico

Cardinal Health

... reinforcement. * Leverage facilitation, instructional design, and content adaptation as tools ... Partner in change enablement by supporting adoption, messaging, and feedback loops related to 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, and why are they important?

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.
What job categories do people searching Postdoctoral In Reinforcement Learning jobs in New Mexico look for? The top searched job categories for Postdoctoral In Reinforcement Learning jobs in New Mexico are:
Postdoctoral In Reinforcement Learning jobs near you
Infographic showing various Postdoctoral In Reinforcement Learning job openings in New Mexico as of July 2026, with employment types broken down into 1% As Needed, 73% Full Time, 20% Part Time, 1% Temporary, and 5% Contract. Highlights an 92% Physical, 1% Hybrid, and 7% Remote job distribution.
Scientific Software Developer (ABQ)

Scientific Software Developer (ABQ)

Stellar Science

Albuquerque, NM • On-site

Full-time

Re-posted 19 days ago

Job description

We hire smart Scientists and Software Engineers who love to create and maintain high quality, extensible code, and want to learn and adopt modern C++ practices.
Support software development in the following domains: computer vision and image processing, image simulation, high power microwave systems modeling and simulation, laser source generation and effects modeling, atmospheric modeling, computational electromagnetics (CEM), space domain awareness (SDA), high performance computing (HPC), and computer aided design (CAD) tools, artificial intelligence (AI) and machine learning (ML) techniques, among others.
Minimum Requirements:
  • B.S. in math, science, engineering field, or computer science
  • Substantial software development experience
  • Object-oriented design and C++ programming experience
  • Adept at learning new paradigms and programming development processes
  • Interest in developing modern, high quality C++20/23 code
  • U.S. citizen, willing to undergo background investigation, and perform some work at government and/or customer sites

Desired:
  • Advanced degree (M.S. or Ph.D.) in science, engineering field, math, or computer science
  • Active security clearance

Experience in any of the following is a plus:
  • Additional languages: Java, Python, TypeScript
  • Relevant libraries: Boost, Eigen
  • Cross-platform software development on Linux, Windows, Mac
  • 3D graphics using OpenGL, Open Scene Graph and/or WebGL
  • User interface development with Qt, Java Swing, Material UI
  • Supercomputing: OpenMP, threads, MPI, GPUs
  • Image processing, imagery analysis, or computer vision, computer aided design (CAD)
  • Aerospace vehicles, orbital mechanics, electromagnetics, space domain awareness
  • Modeling and simulation, including directed energy
  • Machine learning and data analysis using Python (pandas, NumPy, SciPy, scikit-learn), C++, and frameworks such as PyTorch and TensorFlow
  • Reinforcement learning, large language model development, computer vision, data mining, and core ML techniques (classification, regression, clustering)
  • Experience with Advanced Framework for Simulation Integration & Modeling (AFSIM)

A representative sample of your code may be requested early in the evaluation process, e.g. something you've written for work, for a class, or for fun. It should be long enough to help evaluate your programming and software engineering skills.

Apply