Bifold - Berlin Institute For The Foundations Of Learning And Data

Bifold - Berlin Institute For The Foundations Of Learning And Data company information, Employees & Contact Information

The Berlin Institute for the Foundations of Learning and Data (BIFOLD), has evolved in 2019 from the merger of two national Artificial Intelligence Competence Centers: the Berlin Big Data Center (BBDC) and the Berlin Center for Machine Learning (BZML). Embedded in the vibrant Berlin metropolitan area, BIFOLD provides an outstanding scientific environment and numerous collaboration opportunities for national and international researchers. BIFOLD offers a broad range of research topics as well as a platform for interdisciplinary research and knowledge exchange with the sciences and humanities, industry, startups and society. Data management (DM) and machine learning (ML) are the scientific and technical pillars powering the current wave of innovation in artificial intelligence (AI); it is the efficient processing and intelligent analysis of very large, complex, heterogeneous data that has the potential to revolutionize and substantially improve our lives and societies. BIFOLD conducts scalable yet agile foundational AI research. Furthermore, it addresses the emerging challenges and requirements created by the rapidly growing importance of data management and machine learning in practically all areas, from medicine, industry, natural sciences, humanities, e-commerce, and media, to government and society.

Company Details

Employees
68
Founded
-
Address
Ernst-Reuter-Platz 7, C/o Technische Universität Berlin,germany
Industry
Research Services
NAICS
Scientific Research and Development Services
Research and Development in the Physical, Engineering, and Life Sciences
Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)
Keywords
Berlin.
HQ
Berlin, Berlin
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News

Explainable AI reveals Clever Hans effects in unsupervised learning models - Nature

Explainable AI reveals Clever Hans effects in unsupervised learning models Nature

Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields - ACS Publications

Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields ACS Publications

OpenAI CEO defends AI energy consumption for climate solutions - PPC Land

OpenAI CEO defends AI energy consumption for climate solutions PPC Land

News and Calls: June 2025 • Dahlem Research School - Freie Universität Berlin

News and Calls: June 2025 • Dahlem Research School Freie Universität Berlin

Comprehensive genomic profiling requires a blended ecosystem of learning healthcare and clinical trials - Nature

Comprehensive genomic profiling requires a blended ecosystem of learning healthcare and clinical trials Nature

Machine learning offers new framework for heterogeneous catalyst data analysis - Phys.org

Machine learning offers new framework for heterogeneous catalyst data analysis Phys.org

Artificial intelligence improves personalized cancer treatment - lmu.de

Artificial intelligence improves personalized cancer treatment lmu.de

TU Berlin receives multi-million government funding - Science|Business

TU Berlin receives multi-million government funding Science|Business

Google DeepMind Researchers Propose Human-Centric Alignment for Vision Models to Boost AI Generalization and Interpretation - MarkTechPost

Google DeepMind Researchers Propose Human-Centric Alignment for Vision Models to Boost AI Generalization and Interpretation MarkTechPost

Applying AI to cardiovascular medicine - Berlin Institute of Health

Applying AI to cardiovascular medicine Berlin Institute of Health

Fraunhofer HHI, TU Berlin and BIFOLD present new method for Explainable AI - idw - Informationsdienst Wissenschaft

Fraunhofer HHI, TU Berlin and BIFOLD present new method for Explainable AI idw - Informationsdienst Wissenschaft

Reconstructing Kernel-Based Machine Learning Force Fields with Superlinear Convergence - ACS Publications

Reconstructing Kernel-Based Machine Learning Force Fields with Superlinear Convergence ACS Publications

AI in medicine: new approach for more efficient diagnostics - lmu.de

AI in medicine: new approach for more efficient diagnostics lmu.de

Leveraging weak complementary labels enhances semantic segmentation of hepatocellular carcinoma and intrahepatic cholangiocarcinoma - Nature

Leveraging weak complementary labels enhances semantic segmentation of hepatocellular carcinoma and intrahepatic cholangiocarcinoma Nature

Interpolating Nonadiabatic Molecular Dynamics Hamiltonian with Bidirectional Long Short-Term Memory Networks - ACS Publications

Interpolating Nonadiabatic Molecular Dynamics Hamiltonian with Bidirectional Long Short-Term Memory Networks ACS Publications

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