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Postdoctoral In Reinforcement Learning Jobs in Michigan

Eccalon LLC

AI Expert Robotics - onsite

Detroit, MI

Background in autonomous systems, mobile robots, or robotic arms * Experience with computer vision, deep learning, or reinforcement learning * Familiarity with simulation environments (e.g., Gazebo ...

Tata Consultancy Services

AI Researcher

Detroit, MI

... autonomously in the physical world. You will collaborate with interdisciplinary teams of ... From visual perception and SLAM to multimodal sensor fusion and reinforcement learning, you'll be ...

Deloitte

Data Science Consultant

Detroit, MI · On-site

... or Reinforcement Learning Company : Deloitte is a business consulting company that offers audit, consulting, financial advisory, and tax services. Founded in 1845, the company is headquartered in ...

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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.

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Postdoctoral In Reinforcement Learning jobs near you

Artificial Intelligence Engineer

Stefanini

Dearborn, MI • Remote

Other

Posted 3 hours ago

Job description


Stefanini Group is hiring!
Stefanini is looking for an Artificial Intelligence Engineer (Dearborn, MI)
For quick apply, please reach out to Navneet Pathak at /
We are looking for a candidate who is responsible for developing intelligent programs, cognitive applications and algorithms for data analysis and automation, leveraging various AI techniques such as deep learning, generative AI, natural language processing, image processing, cognitive automation, intelligent process automation, reinforcement learning, virtual assistants and specialized programming
ResponsibilitiesUnderstand business requirements and develop AI algorithms, models and programs to solve complex problems, generate recommendations, extract patterns, make predictions, interpret sensor data (images, sound), orchestrate automation and enable self-service capabilities Perform large-scale experimentation and develop data driven applications that translate data into actionable intelligence Drive innovative applications of Artificial Intelligence tools and techniques such as deep learning, generative AI, natural language processing, image processing, cognitive automation, intelligent process automation, reinforcement learning, virtual assistants and specialized programming Research and optimize AI technologies to enhance efficiency and accuracy of data analysis and create more efficient automationExperience in a product engineering role with proven track record of translating business needs into technical specifications for applied AI implementation. Understanding semantic ontologies and how they enable advanced analytics. Experience integrating COTS AI solutions into an enterprise tech stack. Functional understanding of supply chain operations, including demand & capacity planning, logistics, sustainability & risk management, resilience, etc.Act as the primary technical lead for applied AI implementation. Take pre-developed models from internal partners or 3rd-party vendors (COTS) and successfully deploy them within the supply chain Google Cloud Platform space. Work closely with Knowledge Graph engineering teams to map model inputs/outputs to enterprise ontologies. Execute model inference against graph data to provide prescriptions for N-tier supplier risk and material movement. Champion and implement AI-assisted development practices. Use LLM-based tools (e.g., GitHub Copilot, automated PR agents, and AI-generated documentation) to accelerate delivery and ensure high code quality. Design the "connective tissue" between Knowledge Graph updates and model inference engines. Maintain automated pipelines that ensure decision-support tools are always powered by the most current data. Develop reusable integration patterns and data contracts to ensure that AI solutions can be scaled across multiple business units without redundant engineering effort.Proven track record of using AI tools to enhance personal or team productivity (e.g., Agentic workflows, RAG-based requirement synthesis).
Skills RequiredArtificial Intelligence & Expert Systems, Machine Learning, Data Science, Data Modeling, Software Development Lifecycle
Experience Required5+ years of experience in relevant field3+ years of progressive experience in AI/ML, data science, or advanced analytics, with a proven track record of delivering production-grade solutions in large enterprise environments. Strong proficiency in Python and SQL. Familiarity with Graph Query Languages (e.g., Cypher). Demonstrated experience with MLOps principles and tools (e.g., Azure ML, AWS SageMaker, Google Cloud Platform AI Platform, Kubeflow, MLflow) and designing / implementing AI-specific SDLCs. Strong technical expertise in cloud services (Google Cloud Platform/Vertex AI) and data integration patterns
Education Required:Bachelor's degree in computer science, Engineering, Data Science, or a related technical field.
Education Preferred:Master's Degree
**Listed salary ranges may vary based on experience, qualifications, and local market. Also, some positions may include bonuses or other incentives***
Stefanini takes pride in hiring top talent and developing relationships with our future employees. Our talent acquisition teams will never make an offer of employment without having a phone conversation with you. Those face-to-face conversations will involve a description of the job for which you have applied. We will also speak with you about the process, including interviews and job offers.
About Stefanini Group
The Stefanini Group is a global provider of offshore, onshore and near shore outsourcing, IT digital consulting, systems integration, application, and strategic staffing services to Fortune 1000 enterprises around the world. Our presence is in countries like the Americas, Europe, Africa, and Asia, and more than four hundred clients across a broad spectrum of markets, including financial services, manufacturing, telecommunications, chemical services, technology, public sector, and utilities. Stefanini is a CMM level 5, IT consulting company with a global presence. We are a CMM Level 5 company.
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