NEWS
Rethinking AI architecture for molecular simulations
Classical machine learning models for molecular dynamics embed physical principles, such as energy conservation and equivariance, directly into their architectures. These inductive biases have long been seen as essential for reliable simulations. A new study challenges this assumption with a surprisingly simple approach.
ICLR 2026 Conference Contributions
BIFOLD researchers will participate in the fourteenth International Conference on Learning Representations (ICLR), contributing seven workshop papers and seven posters.
EVENTS
kennedy+swan: The Red Queen Effect
Starting January 26, 2026, the foyer of the Rahel Hirsch Center will host a new exhibition dedicated to a topic central to research at BIH and Charité: Artificial Intelligence.
BIFOLD Talk: Attention-based Learned Cardinality Estimator for SPJ queries.
Pengfei Li wants to talk about ALECE, an Attention-based Learned Cardinality Estimator for SPJ queries. The key idea is to capture the implicit relationships between queries and dynamic underlying data through two attention-based modules built on carefully designed data and query featurizations.
*Data collected from 2020 onwards.
OPPORTUNITIES
BIFOLD welcomes job applications from interested parties year-round.
PUBLICATIONS
Nikolai Merkel, Ruben Mayer, Volker Markl, Hans-Arno Jacobsen
EmbedPart: Embedding-Driven Graph Partitioning for Scalable Graph Neural Network Training
Johann-Ludwig Herzog, Mathis Jürgen Adler, Leonard Hackel, Yan Shu, Angelos Zavras, Ioannis Papoutsis, Paolo Rota, Begüm Demir
BigEarthNet.txt: A Large-Scale Multi-Sensor Image-Text Dataset and Benchmark for Earth Observation
Leonardo Medrano Sandonas, David Balcells, Anton Bochkarev, Jacqueline M. Cole, Volker L. Deringer, Werner Dobrautz, Adrian Ehrenhofer, Thorben Frank, Pascal Friederich, Rico Friedrich et al.
Perspective: Towards sustainable exploration of chemical spaces with machine learning
GRADUATE SCHOOL
Based on a highly competitive application process, the BIFOLD Graduate School offers an innovative fast-track PhD Program for students holding a bachelor’s degree, as well as a PhD Program for students with a master’s degree.
INSTITUTIONAL PARTNERS
ASSOCIATED PARTNERS
