PhD on Open-Source Foundation Models

Job description

Snapshot

Artificial Intelligence is reshaping our world. Now you get to shape AI. This large and ambitious project aims to develop a groundbreaking family of advanced, multilingual, open source foundation models based on solid research on model architecture, data quality, scaling, generalizability, finetuning, and safety. It combines the unique expertise of leading AI companies and top academic labs to create models with stronger generalization and finetuning capabilities, as well as enhanced transparency and safety. This will be an inclusive, community-driven project designed to and foster a new wave of innovation and scientific advancement.

The team

The Automated Machine Learning team at TU Eindhoven focusses on cutting-edge research to advance the capabilities of machine learning models, while also democratizing AI and leveraging it to help humanity. We are a team of scientists and engineers who aim to deeply understand, explain, and build AI systems that learn continually and automatically assemble themselves to learn faster and better. In addition to producing highly-cited research published at top academic venues, we build models and systems that are widely used by people every day. This work is part of a large and talented team with world-class labs and experts across Europe, supported by well-known companies with advanced knowledge of LLM development.

The role

We are seeking a proficient and talented PhD student with a true passion for AI. You will play a pivotal role in a team exploring new model architectures, novel efficient finetuning and adaptation techniques, refining data to enhance learning and generalizability, creating benchmarks, and ensuring safety while contributing to state-of-the-art, human-centric LLM development with real-world impact.

Key responsibilities (depending on your expertise):

As part of a team, you will focus on a subset of the following tasks, aligned with your strengths and interests:

• Research and develop innovative foundation model architectures and scaling laws.
• Explore efficient, cutting-edge finetuning and adaptation techniques based on transfer learning, meta-learning, and continual learning.
• Design and optimize data pipelines for pretraining models to enhance generalization.
• Create new benchmarks to assess safety and generalization abilities across diverse applications.
• Develop tools for continuous evaluation, benchmarking, and training monitoring.
• Collaborate to tackle challenging technical problems and publish research in top venues.

What we’re looking for

A self-driven researcher with strong expertise in machine learning. Knowledge of foundation models, LLMs, pretraining, finetuning, transfer learning, meta-learning, and/or continual learning is a plus. You should have strong technical and programming skills and a drive to create new things. We appreciate a collaborative mindset and eagerness to work with a consortium of leading researchers, companies and stakeholders.

Impact

This is your chance to make a real-world impact by advancing open-source foundation models while collaborating with leading AI researchers and innovative companies. Gain invaluable new skills as you help democratize AI, develop cutting-edge models, and create more efficient fine-tuning techniques that empower responsible innovation and drive scientific progress.

Job requirements

• A master’s degree (or an equivalent university degree) in Machine Learning.
• A research-oriented attitude.
• Ability to work in a team and interested in collaborating with international labs.
• Motivated to develop your teaching skills and coach students.
• Fluent in spoken and written English (C1 level).
• A true passion for AI!

Conditions of employment

A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:

• Full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months. You will spend 10% of your employment on teaching tasks.
• Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. €2,901 max. €3,707).
• A year-end bonus of 8.3% and annual vacation pay of 8%.
• High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process.
• An excellent technical infrastructure, on-campus children's day care and sports facilities.
• An allowance for commuting, working from home and internet costs.
• A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates. 

Information and application

About us

Eindhoven University of Technology is an internationally top-ranking university in the Netherlands that combines scientific curiosity with a hands-on attitude. Our spirit of collaboration translates into an open culture and a top-five position in collaborating with advanced industries. Fundamental knowledge enables us to design solutions for the highly complex problems of today and tomorrow. 

Curious to hear more about what it’s like as a PhD candidate at TU/e? Please view the video.

Information

Do you recognize yourself in this profile and would you like to know more? Please contact the hiring manager Joaquin Vanschoren, Associate Professor, j.vanschoren@tue.nl.

Visit our website for more information about the application process or the conditions of employment. Are you inspired and would like to know more about working at TU/e? Please visit our career page.

Application

We invite you to submit a complete application by using the apply button. The application should include a:

• Cover letter in which you describe your motivation and qualifications for the position.
• Curriculum vitae, including a list of your publications and the contact information of three references.

We look forward to receiving your application and will screen it as soon as possible. The vacancy will remain open until the position is filled.