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for a/an University assistant predoctoral - PhD Position in Graph Learning 39 Faculty of Computer Science Startdate: 01.05.2026 | Working hours: 30 | Collective bargaining agreement: §48 VwGr. B1
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | about 11 hours ago
the replicator equation. The candidate(s) may also be required to develop computational and algorithmic platforms to link models to biological data. The project integrates dynamical systems, graph theory, linear
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Description Are you excited about using large-scale AI to accelerate scientific discovery? Join a Horizon Europe project developing next-generation scientific foundation models that combine knowledge graphs
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17 Mar 2026 Job Information Organisation/Company cnrs Department lem3 Research Field Engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 30 Jun
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17 Mar 2026 Job Information Organisation/Company Université de Haute-Alsace Department IRIMAS Research Field Computer science Researcher Profile First Stage Researcher (R1) Positions PhD Positions
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Applied to Neuroscience and Drug Discovery Where to apply Website https://gestiononline.bioef.eus/ConvocatoriasPropiasBiobizkaia/es/Convocatorias… Requirements Research FieldOtherEducation LevelPhD
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) Positions PhD Positions Application Deadline 20 Mar 2026 - 18:00 (Europe/Berlin) Country Germany Type of Contract Temporary Job Status Part-time Is the job funded through the EU Research Framework Programme
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several major types of mobility modeling, with the aim of improving their respective efficiency and usage: four-step models, multi-agent systems and a mobility model developed in the PhD thesis of Louisette
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components. To strengthen our research group, Integrative Food Systems Analysis / Section III, we are currently looking for a committed PhD Student to start on 01/05/2026. The position is part-time (TV-L 13
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. Requirements: PhD completed less than 7 years ago in Computer Science or related areas; experience in machine learning and data science (supervised/unsupervised models, recommendation and evaluation/robustness