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Field
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their connection to the group’s activities in their cover letter. Possible research topics include: Collaborative, federated or decentralized learning/Continual learning and knowledge sharing/Optimization
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collaborators that: Negotiate more effectively by anticipating the downstream effects of each statement. Support complex decision-making in domains where success depends on multi-step strategy. Learn to adapt
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collaborative projects of medium or long duration, during which they learn to manage a project by coordinating activities, distributing work, and making collective decisions. Students generally rely on shared
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objects, by embedding them into a 2 or 3-dimensional space through a representation learning algorithm, has been widely used for data exploratory analysis. It is particularly popular in areas such as
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. The project involves close collaboration with experimentalists who provide the relevant data. Machine learning methods will be applied to link experimental (imaging) data to a mechanistic model. The results
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our education, while benefitting them with our talent supply and collaborative research achievements. The University’s unique applied learning pedagogy integrates work and study, embedding authentic
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Max Planck Institute for Intelligent Systems, Tübingen site, Tübingen | Bingen am Rhein, Rheinland Pfalz | Germany | about 3 hours ago
Job Code: Tü_99 Job Offer from October 21, 2025 The Max Planck Institute for Intelligent Systems (MPI-IS) conducts research to advance intelligent systems that perceive, learn, and interact. Through
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processes that produce energy and raw materials. The Department of Thermodynamics of Actinides is looking for a PhD Student (f/m/d) - Machine Learning for Modelling Complex Geochemical Systems. The job
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for Biomarker Discovery: Develop and fine-tune machine learning algorithms for biomarker discovery, cancer classification, and exploring omics features. Collaborative Research: Work in a multidisciplinary setting
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) and the Wallenberg National Program for Data-Driven Life Science (DDLS ). Our project, Learning 3D Genome Dynamics from Heterogeneous Data , is a 5-year collaboration between researchers at Uppsala