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Type of contract: 5 month contract The appointed person will be expected to be proficient in gene expression analysis. This will include ability to work with large genomic / transcriptomic datasets
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. REQUIREMENTS PhD in Bioinformatics, Computational Biology, or a related field. Strong experience in bioinformatics and analysis of next-generation sequencing data. Proficiency in programming languages such as R
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8 Jul 2026 Job Information Organisation/Company Saarland University Department Data Driven Simulation and Analysis in Material Science Research Field Computer science Researcher Profile Established
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learning modelling expertise with experience in the analysis of challenging large-scale data sets. Experience with cellular imaging data or virology/immunology are a plus.Relevant skills include:•Experience
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are looking for candidates to have the following skills and experience: Essential criteria 1. PhD qualified in a relevant subject area, such as theoretical physics, computational physics, quantum information
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28 Jul 2026 Job Information Organisation/Company University of Galway Research Field Computer science » Other Researcher Profile Recognised Researcher (R2) Positions PhD Positions Application
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modifications, structural proteomics, or systems-level analysis of molecular and cellular biology. Research may involve large-scale public proteomics datasets, data harmonization, cross-study integration
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
the start date, a PhD in Data Science, Computer Science, Electrical Engineering, Electrical and Computer Engineering, or a closely related field. The applicant’s research should focus on machine learning
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on health data as well as training AI models on health data across national borders. Key responsibilities Design, implement and benchmark machine learning models for large-scale health datasets consisting
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on the HASRL framework and the empirical taxonomy developed by the PhD candidate. Model training and fine-tuning. Fine-tune large language models on annotated educational datasets collected during the project