POSITION AVAILABLE

Research Scientist, IBM Corporation, Yorktown Heights, NY: Study and analyze relevant research literature. Create novel, next generation sequential decision making methods using AI planning and Reinforcement learning approaches. Design theoretically sound solutions and algorithms. Develop invention disclosures. Develop scientific articles domain specific, requiring adaptation and often retraining from scratch when moving to a sufficiently different task. Implement software prototypes. Evaluate performance using customer provided and simulated benchmarks. Prepare and present results in internal and customer-facing presentations. Communicate directly with customers to define the requirements and scope of the research project. Collaborate with product teams or clients to engineer the solutions in a shape or form to be utilized by the client. Apply the proposed solution in several practical applications such as Data Science and ML platforms for automated AI pipeline generation and planning for cloud-based modernization of data and applications. Mentor and work with summer interns. Review for top conferences and journals. Invest in efforts at obtaining internal and external funding. Collaborate with internal and external collaborators. Utilize: Artificial Intelligence Planning System (Heuristic search algorithms, Fast Downward Planning System), Reinforcement Learning Algorithms (Model-free/model-based RL algorithms, baseline algorithm implementations in stable baselines library or rllib library), Probabilistic Modelling in Machine Learning (Graphical models, variational optimization), Differential Programming Frameworks (Pytorch, Tensorflow), C++/Python, Latex. Required: Ph.D or equivalent in Computer Science, Engineering or related and one (1) year of experience as a Research Assistant, Teaching Assistant, Graduate Student Researcher or related. One (1) year of experience must include utilizing Artificial Intelligence Planning System (Heuristic search algorithms, Fast Downward Planning System), Reinforcement Learning Algorithms (Model-free/model-based RL algorithms, baseline algorithm implementations in stable-baselines library or rllib library), Probabilistic Modelling in Machine Learning (Graphical models, variational optimization), Differential Programming Frameworks (Pytorch, Tensorflow), C++/Python, Latex. $221,311.92 – $289.200.00 per year. Full time. WJ148.

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