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education to enable regions to expand quickly and sustainably. In fact, the future is made here. Umeå University is offering a PhD position in Computing Science with a focus on machine learning for graph
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perturbation models that combine foundation models (FMs) and graph neural networks (GNNs) to accelerate therapeutic target identification. GenePPS aims to overcome current limitations of perturbation modelling
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graphs (KGs). Contracting requirements: Presentation of the academic qualifications and/or diplomas, if applicable. Enrollment in a PhD degree program. Work plan: The fellowship holder will support WP2
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at the department, together with three PhD students. The successful candidate will work with Senior Associate Professor Olaf Hartig who is a leading researcher in the field of Semantic Web and Knowledge Graph
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the design and analysis of such models. PhD position 1 will focus on developing new graph-theoretic frameworks for analyzing graph learning models, such as Graph Neural Networks or Graph Transformers. PhD
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tools Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 6 Apr 2026 - 23:59 (Europe/Brussels) Country Belgium Type of Contract Temporary Job Status Full-time Hours
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research contributions will include designing algorithms for concept and structure extraction, building neural/graph hybrid models for pedagogical reasoning, implementing ontology-alignment methods for cross
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Sorbonne Université SIS (Sciences, Ingénierie, Santé) | Paris 15, le de France | France | 9 days ago
, basées sur la théorie des graphes et les réseaux neuronaux (en collaboration avec L. Bonati de l'IIT Genova, qui a développé la bibliothèque mlcolvar, https://github.com/luigibonati/mlcolvar ). 2
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-mail and by post will not be considered. Where to apply Website https://academicpositions.com/ad/empa/2026/phd-position-in-hierarchical-graph-n… Requirements Research FieldComputer scienceYears
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for therapeutic intervention. This PhD project will leverage large-scale single-cell RNA-seq and spatial transcriptomics datasets from infection biology to develop models, including transformer-/graph-based models