Research Return Intern

At IBM Research, we are the innovation engine of IBM. Exploring what’s next in computing and shaping the technologies the world will rely on tomorrow. From advancing AI and hybrid cloud to pioneering practical quantum computing, we anticipate challenges and unlock new opportunities for clients, partners, and society. Working in Research means joining a team that accelerates discovery at the intersection of high-performance computing, AI, quantum, and cloud. You’ll collaborate with leading scientists, engineers, and visionaries to push boundaries and turn ideas into reality. With a culture built on curiosity, creativity, and collaboration, IBM Research offers the opportunity to grow your career while contributing to breakthroughs that transform industries and change the world. As a Research Scientist in Artificial Intelligence, you will lead cutting-edge projects in various AI and machine learning areas. You will create and leverage AI techniques to solve analytical problems using rigorous and quantitative approaches. Your primary responsibilities will include: • Conduct Research Projects: Observe and learn from existing research in AI and machine learning areas, such as deep learning, reinforcement learning, and computer vision. Explore new techniques and approaches to solve complex analytical problems. • Develop AI Algorithms: Assist with creating and testing AI algorithms, and participate in proving mathematical properties of these algorithms. Engage with experiments to demonstrate algorithm properties and their impact on user experiences. • Embed Research in Systems: Contribute to embedding research algorithms in usable systems, and support the development of scalable distributed implementations. • Communicate Research: Learn from and engage with technical communities through publications and presentations, and assist with communicating research findings to stakeholders. • Explore AI Frameworks: Gain exposure to frameworks such as Tensorflow, Caffee, Py.Torch, or Theano, and participate in applying these frameworks to research projects. • Exposure to AI and Machine Learning: Familiarity with AI and machine learning concepts, including deep learning, reinforcement learning, and computer vision. • Basic Understanding of Algorithm Development: Interest in creating and testing AI algorithms, and curiosity about mathematical properties of these algorithms. • Familiarity with AI Frameworks: Exposure to frameworks such as Tensorflow, Caffee, Py.Torch, or Theano, and basic understanding of their applications. • Analytical Problem-Solving Skills: Ability to explore and learn from existing research, and participate in solving analytical problems using rigorous and quantitative approaches. • Interest in Technical Communication: Willingness to learn from and engage with technical communities through publications and presentations. • Familiarity with NLP: Exposure to natural language processing (NLP) concepts, including QA, dialog, and other related areas, is beneficial for this role. • Knowledge of Brain-Inspired Algorithms: Basic understanding of brain-inspired algorithms and neuromorphic architectures can be an asset in this position. • Experience with KRR: Exposure to knowledge representation and reasoning (KRR) concepts, including symbolic and trainable logic, can be advantageous.