The ELLIS PhD program is a key pillar of the ELLIS initiative whose goal is to foster and educate the best talent in machine learning and related research areas by pairing outstanding students with leading academic and industrial researchers in Europe. The program also offers a variety of networking and training activities, including summer schools and workshops. Each PhD student is co-supervised by one ELLIS fellow/scholar or unit faculty and one ELLIS fellow/scholar, unit faculty or member based in different European countries, and conducts an exchange of at least 6 months with the international advisor during their degree. One of the advisors may also come from industry, in which case the student will collaborate closely with the industry partner, and spend min. 6 months conducting research at the industrial lab.
Research areas include (but are not limited to) the following machine learning-driven research fields:
- AutoML
- Bayesian & Probabilistic Learning
- Bioinformatics
- Causality
- Computational Neuroscience
- Computer Graphics
- Computer Vision
- Deep Learning
- Earth & Climate Sciences
- Health
- Human Behavior, Psychology & Emotion
- Human Computer Interaction
- Human Robot Interaction
- Information Retrieval
- Interactive & Online Learning
- Interpretability & Fairness
- Law & Ethics
- Machine Learning Algorithms
- Machine Learning Theory
- ML in Chemistry & Material Sciences
- ML in Finance
- ML in Science & Engineering
- ML Systems
- Multi-agent Systems & Game Theory
- Natural Language Processing
- Optimization & Meta Learning
- Privacy
- Quantum & Physics-based ML
- Reinforcement Learning & Control
- Robotics
- Robust & Trustworthy ML
- Safety
- Security, Synthesis & Verification
- Symbolic Machine Learning
- Unsupervised Learning
*APPLICATIONS WILL OPEN IN OCTOBER 2022*
Interested candidates should apply online through the ELLIS application portal by November 15th, 2022, 23:59 CET. Please read the FAQs and webpage before applying. All application documents (application form, documents, references) must be submitted before the application deadline.