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|2025/795 under the scope of the Project Machine Unlearning in Speech Foundation Models: Learning to Forget (LeaF), Refª 2024.14611.CMU , funded Fundação para a Ciência e a Tecnologia, I.P., is now
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approaches for binarized network models, identifying their strengths, limitations, and applicability within privacy-focused machine learning frameworks. Special attention will be given to evaluating
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or related areas with research experience on self-adaptation techniques to enhance the efficiency of machine learning based systems. More in detail, a key requirement for eligibility is provable
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team. Preferential factors: academic performance, with a focus on Machine Learning and Biomedical sciences previous experience (e.g., research, professional, lecturing) in the domains of the grant
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developing statistical and machine learning approaches for the integration of cancer multi-omics data and the analysis of CRISPR-based screens. Responsibilities include designing bioinformatics workflows
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8 Oct 2025 Job Information Organisation/Company INESC ID Research Field Engineering » Biomedical engineering Engineering » Computer engineering Researcher Profile First Stage Researcher (R1
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developing statistical and machine learning approaches for the analysis of CRISPR base editing screens and their integration with large-scale genomic annotations derived from variant effect predictors
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19 Sep 2025 Job Information Organisation/Company INESC ID Research Field Engineering » Computer engineering Researcher Profile First Stage Researcher (R1) Positions Master Positions Country Portugal
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19 Sep 2025 Job Information Organisation/Company INESC ID Research Field Engineering » Computer engineering Researcher Profile First Stage Researcher (R1) Positions Master Positions Country Portugal