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background knowledge in statistical genetics, machine learning, omics data analysis, and computational biology. Additional knowledge in transcriptomics, statistics, and foundation models is also desired. Fixed
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in continual learning settings. The core focus is on leveraging Reinforcement Learning (RL) to make the training and deployment of LLMs more computationally and sample efficient. This approach aims
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to the 30th September 2026. We are looking for outstanding machine learning researcher to join the Torr Vision Group and work on AI Scientists: systems that use foundation models, AI agents, and robotics
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lead the development of multi-modal MRI foundation models that integrate imaging data and radiology reports. Using advanced deep learning techniques—including vision-language architectures (e.g., CLIP
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Aalto University is inviting applications for a Postdoctoral researcher in molecular machine learning. The successful applicant will join the research group of Professor Juho Rousu. The position
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machine learning model to rule out heart attacks in the emergency room, which has the potential to translate to large savings for healthcare systems in the world, (2) a computational modelling to assist in
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machine learning methods to model changes in the brain over the lifespan, including brain structure and function, and how those changes relate to environment and genomics. About the Role The post is funded
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We are looking for a Postdoctoral Research Associate reporting to the Principal Investigator Prof Yee-Whye Teh, they will be a member of the Oxford Computational Statistics and Machine Learning
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-learning approaches. In particular, the postholder will work on approaches that learn mappings between mouse, marmoset, macaque, and human cortex, and enable the comparison of the macroscopic cortical
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fundamental research, we create widely used open-source software including autodE, cgbind/C3, and mlp-train. Our recent advances in Machine Learning Interatomic Potentials (MLIPs) form the foundation of our ERC