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publications. You will work alongside PhD students and interact with experimental partners across the NAP4DIVE consortium. You will have access to the DelftBlue high-performance computing cluster. This position
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graphs and related structures, limit theorems, stochastic calculus and applications, for example in machine learning and mathematical statistics Participation in the scientific activities of the department
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, and clinical safety datasets Implement graph-based retrieval-augmented generation (RAG) methods to enhance knowledge extraction and information synthesis Develop cross-pathway analytical methods using
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(23:59 CET) Expected date of employment: October 1st, 2026 Terms of employment: 1. Full-time position. 2. Employment period: 24 months. 3. Place of work: Warsaw. Required qualifications: 1. PhD in
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information are provided here: https://www.eu4greenfielddata.eu/phd-positions-application/list-of-phds Deadline: 15th April 2026 Where to apply Website https://www.eu4greenfielddata.eu/phd-positions-application
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. A particular focus of the project will be on: 1) Graph Neural Networks for cosmology, neutrino and/or collider physics, 2) Domain adaptation methods / model robustness, 3) Uncertainty quantification
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the period of the project, not exceeding the maximum period set by FCT for such grants. RENEWAL Renewable is subject to performance if the candidate is enrolled in a PhD program - art. 6º, n.4 c) https
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to capture the spatial complexity of tumor organization and its relationship to treatment response. This PhD project aims to develop robust multimodal predictive models of platinum resistance using a large
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | 2 months ago
programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Area of research: PHD Thesis Job description:PhD Candidate (f/m/x) - AI/ML Drug Discovery for Brain
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conclude on December 31st 2029. The goal of this research effort is to apply machine learning (ML) techniques, in particular (equivariant) graph neural networks to accelerate the creation of all physical