Do you want to take part in shaping the future of AI-driven materials discovery? We are seeking a talented and highly motivated student for a Joint PhD at Bar-Ilan University (BIU), to conduct cutting-edge research in collaboration with our teams, on next-generation Foundation Models for Materials Science.
The work will focus on developing novel multi-modal foundation models for materials discovery, leveraging large-scale data, representation learning, and uncertainty estimation. The candidate will work closely with academic and industry researchers, contributing to both methodological advances and real-world impact across scientific and industrial domains.
What youll be doing:
Conduct research in deep learning and AI for materials science, with a focus on foundation models, multi-modal learning, and representation learning.
Develop and train large-scale models for materials discovery and prediction tasks.
Collaborate with researchers and engineers across and academia.
Lead independent research under academic supervision, while contributing to team efforts.
Publish results in top-tier conferences and journals, and present findings clearly and effectively.
Requirements: A student currently pursuing a Ph.D., and a graduate of a Masters degree (M.Sc.) in Computer Science, Electrical Engineering, Materials Science, or a related field
Strong background in deep learning and machine learning.
Experience with foundation models, multi-modal learning, or self-supervised learning.
Strong communication skills and ability to work collaboratively in interdisciplinary teams.
Ways to stand out from the crowd:
Background or strong interest in materials science and chemistry.
Proven experience in building or training large-scale foundation models.
Experience with uncertainty estimation, generative models, or scientific machine learning.
Strong programming skills in Python and deep learning frameworks (e.g., PyTorch).
This position is open to all candidates.