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decomposed into modular sub-components that can be either process-based models and/or deep learning models. MCL has the flexibility to replace any uncertain process description with a deep learning model
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for screening purposes and cell-based therapies. We will develop methods for modelling missing not at random (MNAR) observations and quantifying uncertainty using Bayesian methods and deep learning architectures
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, computational mechanics, computer science, applied mathematics or similar Strong experience with deep learning, e.g. PyTorch, JAX, TensorFlow, and probabilistic methods Familiarity with graph neural networks
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Profile: A Master`s degree and an excellent PhD degree in Biochemistry, Chemistry, or a related Molecular Science Proven Track Record in Machine Learning, Molecular Simulations, Chemoinformatics
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-position-deep-sea-ecological-modelling Work Location(s) Number of offers available1Company/InstituteInstitut de Ciències del Mar (ICM), CSIC, BarcelonaCountrySpainCityBarcelonaGeofield Contact State/Province
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 16 hours ago
and related activities. Strong programming skills are a must, including experience with machine learning. Good written and oral communication skills in English are important. Point of Contact Mikeala
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Swarm Intelligence, Reinforcement Learning and Optimization Techniques. As a Postdoctoral researcher, you will: Lead cutting edge research in Swarm Intelligence and Machine Learning, addressing challenges
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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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] Position Title: Position Type: Postdoctoral Position Location: Lemont, Illinois 60439, United States of America [map ] Subject Area: High Energy Physics / Machine Learning Appl Deadline: none (posted 2025