Pre-doctoral Position

Role Description

The IBM Research Zurich Laboratory together with TU Wien have a fantastic opportunity for a highly motivated and talented Doctoral Candidate to join our teams and contribute to a groundbreaking project focused on developing reliable and grounded Multimodal Large Language Models (LLMs). This research directly aligns with IBM’s broader efforts to build enterprise-ready AI solutions, specifically leveraging and contributing to the capabilities of the IBM Granite model suite. The project will explore novel LLM post-training methodologies incorporating Reinforcement Learning (RL) and visual reasoning capabilities to ensure LLMs are effectively grounded in real-world data and can operate reliably across diverse applications, ultimately contributing to the development of robust and scalable models.

Core Activities

During the doctoral studies, the DC will perform research activities in some of the following areas:

• Develop and implement advanced training algorithms for LLMs incorporating Reinforcement Learning to optimize for grounding and performance – with a focus on techniques applicable to the Granite architecture
• Investigate techniques for integrating diverse data modalities into LLM training pipelines with focus on images and visual tasks
• Investigate Retrieval-Augmented generation for multimodal synthetic data generation
• Evaluate LLM performance through rigorous experimentation, benchmarking, and analysis, with a focus on enterprise-relevant metrics
• Contribute to the publication of research findings in leading AI conferences and journals
• Collaborate closely with senior researchers, engineers, and other teams within IBM Research Zürich and TU Wien
  

The candidate will be hired and hosted by the IBM Research Zurich Laboratory, in the AI Automation Team, led by Cristiano Malossi . The candidate will be supervised by Mattia Rigotti (IBM) and Katja Hose (TUW), having the opportunity to work in a unique corporate environment, acquire experience in several areas, publish in top international journals and conferences, as well as learn how to apply machine learning and AI on real business cases. The candidate will also have a chance to spend a period of a few months at Université Paris Cité during a planned secondment.

Minimum Qualifications (mandatory)

• Outstanding university track record, with background in Computer Science, Artificial Intelligence, Robotics, Cognitive Science, or a related field
• Proficiency in Python and experience with deep learning frameworks (preferably PyTorch)
• Proficient working in Unix/Linux environments
• Team player, self-motivated with a passion for technology and innovation
• Ability to speak and write in English fluently
  
  

Preferred Qualifications

• Experience in machine learning, Deep Learning, Reinforcement Learning, Large Language Models (LLMs)
• Proved experience in collaborative software development projects, such as contribution to open-source libraries, or equivalently demonstrated ability to implement ideas and complex algorithm with attention to detail and code quality
• Independent worker with the ability to effectively operate with flexibility in a fast-paced, constantly evolving team environment
• A strong publication record is highly valued
  
  

Conditions to apply

The researcher must not have resided or carried out their main activity (work, studies, etc.) in the country of the host organization (Switzerland) for more than 12 months in the 3 years immediately prior to the start date of the PhD.

This position is part of the European program ARMADA . If you are interested in this exciting position, please submit your most recent curriculum vitae and a short motivation letter. The interview process will include a programming interview and a ML/AI interview.

Diversity

IBM is committed to diversity at the workplace. With us you will find an open, multicultural environment. Excellent flexible working arrangements enable all genders to strike the desired balance between their professional development and their personal lives.

How to apply

Please submit your application through the link below.