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10 Feb 2026 Job Information Organisation/Company Institute of Inorganic Chemistry, SAS Department Department of Molten Systems Research Field Chemistry » Physical chemistry Chemistry » Inorganic
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methods and procedures for use in the joint project Evaluation, analysis, and visualization of data Documentation of laboratory work Collaboration with other project partners and regular presentation
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engineeringYears of Research ExperienceNone Additional Information Website for additional job details https://emploi.cnrs.fr/Offres/Doctorant/UMR7239-THIRIC-003/Default.aspx Work Location(s) Number of offers
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the processing and analysis of the data resulting from the experimental tasks. Participation with the research group's investigators in the final discussion of the results. Where to apply Website https
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characterization of blood cells and tissue samples, collaboration in omics data analysis. Where to apply E-mail marcin.poreba@pwr.edu.pl Requirements Research FieldChemistry » OtherEducation LevelPhD or equivalent
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an interest in image analysis and instrumentation. Website for additional job details https://emploi.cnrs.fr/Offres/Doctorant/UMR6251-ANAGAU-002/Default.aspx Work Location(s) Number of offers available1Company
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well as experience in omics data analysis, and possesses solid English-language skills. Experience with programming, preferably Python and R, is required. Experience with deep learning frameworks, such as JAX
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, that combines diffusion and transformer models, there are clear indications that the analysis of this data can be automated. This will open new avenues in data interpretation and building predictive models
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significant lab experience. Selection process For further information or to apply please email a PDF copy of a brief cover letter and CV, to jmcgoura@tcd.ie by 5th May. Website for additional job details https
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, that combines diffusion and transformer models, there are clear indications that the analysis of this data can be automated. This will open new avenues in data interpretation and building predictive models