Short-term Research Coop

Introduction A career at IBM provides the rare opportunity to help define a new industry. You will be working with AI researchers on improving the reliability of foundation models. This position is research-oriented. Outcomes of this position include publications and potential IBM product opportunities. Your Role And Responsibilities As a Research Scientist specializing in Artificial Intelligence, you will lead cutting-edge projects in various AI and machine learning areas, creating and leveraging innovative techniques to solve complex analytical problems. You will apply rigorous and quantitative approaches to develop and embed research algorithms in usable systems. Your Primary Responsibilities Will Include Develop AI Techniques: Create and leverage AI techniques, including machine learning and NLP, to solve analytical problems using rigorous and quantitative approaches. Conduct Experiments: Design and conduct experiments to demonstrate the properties of algorithms and evaluate their impact on user experiences. Embed Research Algorithms: Integrate research algorithms into usable systems, ensuring seamless functionality and optimal performance. Communicate Research: Share research findings with technical communities through publications in top-tier conferences and journals, such as NIPS, CVPR, and ICML. Apply Expertise: Utilize expertise in frameworks like Tensorflow, Caffe, PyTorch, or Theano to drive project success. Preferred Education Bachelor's Degree Required Technical And Professional Expertise AI Technique Development: Exposure to creating and leveraging AI techniques, including machine learning and NLP, to solve analytical problems using rigorous and quantitative approaches. Algorithm Experimentation: Experience working with designing and conducting experiments to demonstrate the properties of algorithms and evaluate their impact on user experiences. Research Algorithm Integration: Exposure to integrating research algorithms into usable systems, ensuring seamless functionality and optimal performance. Technical Community Engagement: Experience with sharing research findings with technical communities through publications in top-tier conferences and journals. Framework Proficiency: Exposure to utilizing expertise in frameworks like Tensorflow, Caffe, PyTorch, or Theano to drive project success. Preferred Technical And Professional Experience Framework Expertise: Exposure to utilizing expertise in frameworks like Tensorflow, Caffe, PyTorch, or Theano to drive project success, creating and leveraging AI techniques, and solving analytical problems using rigorous and quantitative approaches. Technical Community Involvement: Experience with sharing research findings with technical communities through publications in top-tier conferences and journals, such as NIPS, CVPR, and ICML, to communicate research and stay updated on industry advancements. Algorithm Development: Exposure to developing and experimenting with algorithms, including designing and conducting experiments to demonstrate algorithm properties and evaluate their impact on user experiences.