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Postdoctoral In Reinforcement Learning Jobs in Virginia

Virginia Tech

Postdoctoral Associate

Blacksburg, VA

This position is part of a growing program integrating research, Extension, and experiential learning in agribusiness and agricultural finance. The postdoctoral associate will: Work with unique ...

Virginia Tech

Postdoctoral Associate

Blacksburg, VA

Postdoctoral Associate Apply now Back to search results Job no: 536321 Work type: Research Faculty ... learning. Required Qualifications - Ph.D. in Chemistry, Chemical Engineering, Materials Science ...

Virginia Tech

Postdoctoral Associate

Blacksburg, VA

Postdoctoral Associate Apply now Back to search results Job no: 536195 Work type: Research Faculty ... The position involves conducting research in federated learning, and network optimization for 6G ...

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Postdoctoral In Reinforcement Learning information

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 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 popular job titles related to Postdoctoral In Reinforcement Learning jobs in Virginia? For Postdoctoral In Reinforcement Learning jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Postdoctoral In Reinforcement Learning jobs in Virginia look for? The top searched job categories for Postdoctoral In Reinforcement Learning jobs in Virginia are:
What cities in Virginia are hiring for Postdoctoral In Reinforcement Learning jobs? Cities in Virginia with the most Postdoctoral In Reinforcement Learning job openings:
Postdoctoral In Reinforcement Learning jobs near you

Artificial Intelligence (AI) Engineer

TechSur Solutions

Reston, VA • On-site

$119.10K - $143K/yr

Full-time

Posted 12 days ago

Job description

Company Description
TechSur Solutions is a digital services company whose mission is to enable digital transformation for our customers to improve quality and efficiency. Based in the DC metropolitan area, TechSur specializes in advanced cloud services, modernization for both IT structures and applications, leveraging Agile development, and Data Analytics. Since we were formed in August of 2016, we have supported multiple impactful and exciting government programs.
Job Description
Role: Artificial Intelligence (AI) Engineer
Client: US Courts
Duration: Full time position
Location: Reston, VA (Hybrid - 3x/Week)
Company Overview
TechSur Solutions is a digital services company whose mission is to enable digital transformation for our customers improving quality and efficiency. Based in the DC metropolitan area, TechSur specializes in advanced cloud services, modernization for both IT structures and applications, leveraging Agile development, and Data Analytics. Since we were formed in August of 2016, we have supported multiple impactful and exciting government programs.
Job Description
We are seeking a highly motivated AI Engineer with a strong focus on OpenAI technologies to join our growing team. The ideal candidate will have a deep understanding of AI models, multi-agent systems, and the ability to design, develop, and implement intelligent agents capable of autonomous problem-solving and decision-making. Your work will contribute to creating innovative AI-driven applications and solutions that integrate cutting-edge advancements in artificial intelligence, including GPT models, reinforcement learning, and natural language processing.
Job Responsibilities
  • Design and develop AI agents leveraging OpenAI's GPT models and APIs to solve complex problems in real-world environments.
  • Collaborate with cross-functional teams to integrate AI agents into products, services, and tools.
  • Implement and fine-tune natural language processing (NLP) capabilities for AI agents, improving their comprehension and interaction with users.
  • Develop autonomous, multi-agent systems capable of communicating, learning, and collaborating to perform tasks and achieve goals.
  • Create pipelines for training, fine-tuning, and deploying AI models on various platforms, ensuring scalability and efficiency.
  • Research and integrate reinforcement learning techniques to improve agent performance and adaptability.
  • Analyze, debug, and optimize AI agents for performance, robustness, and scalability.
  • Stay current with the latest advancements in AI, ML, and NLP, particularly OpenAI's technology stack.
  • Contribute to creating best practices and documentation for the development and deployment of AI agents.

Required Skills/Qualifications
  • 3+ years of experience in AI/ML development, with a focus on AI agents, multi-agent systems, or autonomous systems.
  • Strong experience with OpenAI GPT models, APIs, and NLP technologies.
  • Proficiency in Python and familiarity with AI/ML frameworks such as TensorFlow, PyTorch, or similar.
  • Hands-on experience with reinforcement learning, deep learning, or generative models.
  • Familiarity with cloud platforms (e.g., AWS, GCP) and containerization tools (e.g., Docker, Kubernetes).
  • Strong problem-solving skills and the ability to work in a fast-paced, collaborative environment.
  • Excellent written and verbal communication skills, with the ability to explain technical concepts to non-technical stakeholders
  • Strong programming skills in languages such as Python, R, or Java.
  • Experience with AI/ML frameworks and libraries such as TensorFlow, Keras, PyTorch, or scikit-learn.
  • Strong problem-solving skills and the ability to work on multiple projects simultaneously.
  • Excellent communication and teamwork abilities.

Preferred Experience
  • Experience with large-scale model deployment in production environments.
  • Knowledge of ethical AI development practices and responsible AI usage.
  • Prior experience in developing conversational agents, virtual assistants, or autonomous systems.
  • Understanding of multi-agent coordination and communication protocols.
  • Passion for cutting-edge AI technologies and their applications.
  • Experience working with federal clients or within the government sector is preferred.

Qualifications
Education
  • Bachelor's or master's degree in computer science, Data Science, Artificial Intelligence, or a related field
  • Years of experience can be considered in lieu of degree

Additional Information
All your information will be kept confidential according to EEO guidelines.

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