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Superior Técnico, Portugal and 27 associated partners (from 10 countries) Format: double PhD degree, granted by two universities in Europe; see the list of main partners: https://www.eu4greenfielddata.eu
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-driven biocatalysis and accelerate bioprocess development. DC1: Machine learning-guided multiparametric optimisation of cytochrome P450 monooxygenase PhD enrolment: Technical University of Denmark DC2
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of the ERC Consolidator project AUTOMATIX (see details below), we are seeking a PhD candidate to develop machine learning approaches for constitutive modeling. Context With the advent of machine-learning (ML
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the DFG Priority Programme “Molecular Machine Learning” and embedded in the research project “Multi-fidelity, active learning strategies for exciton transfer in cryptophyte antenna complexes”. The PhD
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machine learning and computer simulations. The focus of the PhD project will lie on developing machine learning models for clustering, classification, regression and reinforcement tasks to work with
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-resolution (SR) technologies influence human and machine-based facial identification. The PhD will combine behavioural experiments, machine learning, and explainable-AI methods to answer questions: 1. Do SR
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of results at conferences - interaction with team members and international collaborators The Machine Learning for Integrative Genomics team (https://research.pasteur.fr/en/team/machine-learning
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University Medical Center of the Johannes Gutenberg University Mainz | Mainz, Rheinland Pfalz | Germany | 4 days ago
), is offering a fully funded PhD position in the area of statistical learning, machine learning, and survival analysis applied to large-scale proteomics and multi-omics cohort data. The PhD project
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, and advanced machine learning in the engineering domain. Generative AI substantially changes the way engineers interact with and benefit from AI and access domain-specific knowledge, marking a phase
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Job related to staff position within a Research Infrastructure? No Offer Description PhD position on physics-based machine learning modeling for materials and process design Reference code: 2026/WD 1