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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.
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Postdoctoral In Reinforcement Learning jobs near you

Reinforcement Learning Engineering Intern

Persona AI

Pensacola, FL • On-site

$14.25 - $19/hr

Full-time, Internship

Posted 6 days ago

Job description

Reinforcement Learning Engineering Intern
Location: Downtown Pensacola, FL
Type: Full-time Internship, 40 hours/week
About the Internship
The Reinforcement Learning Engineering Internship is an opportunity for Bachelors and Masters candidate students to join and contribute to the Persona team as we develop our industrial humanoids. Our objective is to provide each intern with a positive learning environment, hands-on experience with humanoids, and ownership over their own project direction. We are looking for students with an excitement for learning, technical excellence, and creative problem-solving skills.
Each intern will have a designated mentor to provide guidance and assistance in developing and making progress towards a target goal. We have a strong bias for projects that lead to software, controls, or policies deployed on our hardware and extending the capabilities of our systems. Projects will be jointly planned by the intern and their mentor to build on the intern's background, extend their experience to new areas of interest, and fit into the broader goals of the Persona reinforcement learning team.
Role Description
For this role, the specific tasks will be defined prior to the start date by the mentor and the intern based on their experience, proficiency, and personal interests. The scope may also be adjusted to fit the project within the intern's time-frame. We encourage interns to share their interests even if they may be entirely different from their technical background. Some example general tasks that may be a part of any project are described below:
  • Develop new simulation training environments
  • Design new behaviors or extend capabilities for the Persona robots
  • Deploy to hardware, log data, and analyze results
  • Create or implement new algorithms for modeling, training, sensing, or deployment
  • Characterize hardware sensors, actuators, and general robot parameters
Qualifications
  • Current Undergraduate or Masters student
  • Software proficiency in Python, C/C++, Java, or Rust
  • Experience with basic machine learning concepts
Bonus Experience
  • Worked with Pytorch or similar
  • Physics simulator experience such as IsaacLab/IsaacSim, Mujoco, or similar
  • Deployed controls software to robot hardware
  • Trained policies with reinforcement learning
  • Worked with motion diffusion models or VLAs
  • Experience with character animation
  • Worked on vision or localization
Open Technical Areas
  • Perception
  • Locomotion
  • Manipulation
  • Motion Planning
  • Imitation Learning
  • Motion Retargeting
  • Sim-to-Real Modeling
Application Timeline
We are accepting applications on a rolling basis. We will interview and make offers for upcoming intern cohorts until we fill all openings. We will close the application process for an upcoming cohort approximately 3 months before the start of the cohort and recommend applying approximately 6 months in advance.
Note: we are no longer accepting applications for Fall 2026.
The interview process we are currently following involves two interviews. First, a phone pre-screen with a member of our staff. Second, a presentation and discussion interview with one to two of our engineers. The presentation is meant to be informal and give an opportunity for you to share your background, experiences, and interests. We like the chance to see pictures and videos of your projects and hear what part of robotics excites you most! We will also give an overview of the work we are doing here at Persona AI and leave time for you to ask us questions.
We aim to get back to you as soon as we can but it may take a few weeks, especially in between cohorts. Please know we are working on it and will get back to every application!

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