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The Machine Learning for Integrative Genomics team at Institut Pasteur, headed by Laura Cantini, works at the interface of machine learning and biology, developing innovative machine learning
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(“overparameterized”) machine learning models, like probabilistic graphical models, deep neural networks, diffusion models, transformers, e.g. large language models, etc. SLT is based on the geometrical understanding
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management and machine learning-based integration of multi-omic datasets. Our goal is to identify predictive signatures and develop treatment response models to enable biomarker-guided clinical trials
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modelling knowledge, incorporate reliability/uncertainty, and/or explainable models. The position is in the Digital Signal Processing and Image Analysis Group, Section for Machine Learning, Department
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Project Overview We are hiring highly motivated and talented Postdoctoral Associates who are interested in advancing the state of the art in resource-efficient machine learning at the Singapore-MIT
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Vision, with focus on multimodal learning; Deep generative models, e.g., GANs, diffusion models, encoder-decoder architectures, optimal transport models, Flow Matching; ML and CV approaches with a
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French National Research Institute for Agriculture, Food, and the Environment (INRAE) | Marcy, Picardie | France | about 14 hours ago
rapidly updated. In this context, you will explore ways to overcome these technical challenges by developing hybrid models that combine mechanistic modelling, phylodynamics, and machine learning, with
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French National Research Institute for Agriculture, Food, and the Environment (INRAE) | Montpellier, Languedoc Roussillon | France | about 15 hours ago
with skills in statistics and modelling through machine learning (Agronomy joint research unit, MIA joint research unit, Wageningen University & Research); (ii) national and international laboratories
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plate array microscope for simultaneous time-lapse video microscopy, enabling high-throughput single-cell analyses of rapidly migrating cells. You will be responsible for Develop new machine learning
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student in areas related to Computer Science. Basic experience in health data analysis. Python programming. Knowledge of machine models and deep learning. Intermediate English level. Specific Requirements