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FLEX/FFPE), ATACseq, nCounter panels, spatial transcriptomics, ChIP-seq, cut-and-run and others Apply machine learning algorithms to clinical multi-omic datasets. Assist with collaborative and service
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University Abu Dhabi, seeks to recruit a motivated research or postdoctoral associate to lead the design, development, and validation of material, control systems, and algorithms for next-generation soft
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) Positions PhD Positions Application Deadline 19 Jan 2026 - 23:59 (Africa/Abidjan) Country Germany Type of Contract Temporary Job Status Full-time Hours Per Week 39,83 Offer Starting Date 1 Apr 2026 Is the job
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fellowship (BI) for a PhD Student 1 Research Fellowship(s) (BI) is(are) open at the FCiências.ID – Associação para a Investigação e Desenvolvimento de Ciências, for the project/R&D institution “ROBERTA
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for applicants from outside of EU/ EEA countries and exemptions from the requirements: https://www.mn.uio.no/english/research/phd/regulations/regulations.html#toc8 Grade requirements: The norm is as follows
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) ● Dr. A. Proust (Mistras, France) Secondments (1 to 6 hosting months) Contact information ● tahar.kechadi@ucd.ie ● guillaume.charrier@inrae.fr How to apply https://www.eu4greenfielddata.eu/phd-positions
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algorithms, building models, and deploying systems. A PhD in a related field may substitute for two years of the required experience. Experience must include at least three (3) years of managing projects and
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at: https://www.umu.se/en/department-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data driven models
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algorithms, and experimental systems research, and is closely connected to advanced-level teaching in computer systems and cybersecurity. About the research project This doctoral student position is part of a
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algorithms, architectures, and learning strategies that fundamentally challenge existing resource constraints in large-scale AI systems. Prototype, implement, and rigorously evaluate complex machine learning