Artificial intelligence today is confined to the representation of the world found in datasets. We’re building a new class of AI systems designed to gain a grounded understanding through direct interaction with the physical world.
DVPS is one of the most heavily backed projects in the Horizon Europe program, bringing together 20 leading organizations across 9 countries to advance the science and engineering of multimodal foundation models (MMFMs).
We are developing an open-source toolkit to streamline the design, pre-training, fine-tuning, and modality expansion of MMFMs. It supports the reuse and composition of pre-trained models, reducing development time and cost while enhancing adaptability across modalities.
Enhancing how AI understands and communicates by combining speech, text, video, and gestures into inclusive, multilingual, and adaptive language technologies.
Overcoming the limitations of traditional models by unifying diverse cardiovascular data into multimodal AI systems that enhance diagnosis, prediction, and treatment planning.
Empowering geo-intelligence by fusing diverse spatial, sensor, and textual data into multimodal AI systems for smarter disaster response, urban planning, and environmental forecasting.
Take part in designing the next generation of AI that perceives, understands, and interacts with the world. Join us in shaping intelligent systems that go beyond words and into reality.
Lead Deep Learning Scientist
With several years of proven research experience, join Translated as the lead of our DVPS team, guiding a group of scientists from the DVPS consortium as we navigate our roadmap, contributing to the state of the art, and strategically planning roadmap updates to match an evolving technical landscape.
Senior Deep Learning Scientist
With a PhD in Deep Learning, join us to define our research directions on DVPS, lead experiments on Europe’s largest HPC infrastructure and promote our findings to the scientific community and world at large.
Senior Deep Learning Scientist
With a PhD in Deep Learning, join us to define our research directions on DVPS, lead experiments on Europe’s largest HPC infrastructure and promote our findings to the scientific community and world at large.
