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Summer Internship in Deep Reinforcement Learning-PhD

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Description

At P&G we leverage advanced machine learning methods to solve challenging Research and Development problems. These
challenges range from developing smart products, personalizing consumer experiences and understanding our consumers in depth.
During this internship you will have multiple projects on which you will leverage deep reinforcement learning and other
advanced machine learning methods. You will be part of a diverse, global team with similar backgrounds and projects with
which you can collaborate to find solutions. You will use tools such as Python and Tensorflow.

Key Priorities, Deliverables, or Outcomes of This Position:


1) Apply deep reinforcement learning to recommender systems and/or process development challenges

2) Apply advanced machine learning methods and data visualizations to challenges in Research and Development

3) Share machine learning and statistical applications with team members



Qualifications
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, protected veteran status, disability status, age, sexual orientation, gender identity and expression, marital status, citizenship, HIV/AIDS status or any other legally protected factor.
Immigration sponsorship is not available for this role. As a general matter, Procter & Gamble does not sponsor candidates for nonimmigrant visas or permanent residency. However, Procter & Gamble may make exceptions on a discretionary basis. Any exceptions would be based on the Company's specific business needs at the time and place of recruitment as well as the particular qualifications of the individual.

Procter & Gamble participates in e-verify as required by law.

Qualified individuals will not be disadvantaged based on being unemployed.
**Please include your resume and a brief summary of your research in the resume text field (maximum characters 64,000).**
Job locations:
Cincinnati, Ohio
Job Type: Full-time Req No: RND00003879
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