Fully Funded PhD Positions in Machine Learning & AI for Science

ELLIS; Wien; Hybrid; EUR 42,000 - 54,000; Full time

Benefits offered by this job

Competitive salary

Austrian social benefits

Job summary

ELLIS invites applications for fully funded PhD positions in machine learning and AI for science in Vienna. Researchers will explore geometric deep learning, generative flows, and graph ML while decoding biological systems, with access to a large GPU cluster and collaboration with leading faculties.

PhD tracks are awarded by TU Wien or University of Vienna, for four years, with a competitive salary and Austrian social benefits.

Qualifications

• PhD candidates expected to contribute to ML research for science and publish in top venues.
• Interdisciplinary collaboration with computational and life sciences.
• Strong foundation in ML theory and hands-on experimentation.

Responsibilities

• Develop principled ML methods for biological systems.
• Publish research in NeurIPS/ICML/ICLR and related venues.
• Collaborate with wett-lab and experimental teams to validate models.

Skills

Geometric DL

Graph ML

Generative modeling

Job description

Fully Funded PhD Positions in Machine Learning & AI for Science

Develop foundational machine learning—spanning geometric deep learning, generative flow matching, and foundation models—to decode, model, and program biological systems at Europe’s newest AI-native research institute.

Core ML Research Directions

PhD researchers focus on principled ML methodology with publications at top venues (NeurIPS, ICML, ICLR):

• Geometric Deep Learning & Equivariance: Symmetry-aware representations, invariant architectures, and neural differential equations over non-Euclidean manifolds and 3D molecular structures (SE(3)SE(3)SE(3)).
• Generative Modeling & Continuous Dynamics: Flow matching, continuous-time diffusion, and optimal transport for macromolecular design and complex cellular trajectories.
• Relational Foundation Models & Graph ML: Theoretical expressiveness and generalization of GNNs, graph transformers, and large-scale relational foundation models.

Mentorship & Faculty

Work with leading ML faculty led by Michael Bronstein (Scientific Director for AI; ELLIS Fellow; Oxford) alongside PIs with research backgrounds from Mila, MIT, Oxford, Caltech, and EPFL.

Compute & Research Environment

• Large-Scale Compute & Frontier AI Stack: Dedicated next-generation GPU cluster providing unconstrained compute for large-scale training, paired with direct access to a wide variety of state-of-the-art closed and open-weight LLMs / foundation models.
• Collaborative AI-for-Science Wet Lab: Work hand-in-hand with in-house experimentalists and robotic automation facilities to rapidly validate model predictions and generate high-throughput data in a true closed-loop setup.
• Academic Ecosystem: Strong partnerships with TU Wien, University of Vienna, CeMM, and international research hubs.

Key Information

• Contract: 4 years, fully funded (competitive salary + Austrian social benefits)
• Degree Track: PhD in Computer Science / Machine Learning awarded by TU Wien or University of Vienna
• Location: Vienna, Austria (consistently ranked the world’s most livable city)
• Start Date: 1 September 2027
• Application Deadline: 1 November 2026

We value diverse perspectives, intellectual curiosity, and a strong commitment to interdisciplinary collaboration across computational and life sciences. We warmly welcome applicants of all backgrounds and nationalities.