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. Become part of a team making a real impact in precision oncology! Recent illustrative work The work will extend on recent research entitled “Unbiased Drug Target Prediction Reveals Sensitivity
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work The work will extend on recent research entitled “Unbiased Drug Target Prediction Reveals Sensitivity to Ferroptosis Inducers, HDAC and RTK Inhibitors in Melanoma Subtypes” (Pla I, Szabolcs BL
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analyses to explore potential pathways for behavior change and identify key factors that influence success. Applying machine learning, statistical techniques, and AI to analyze data, predict events, and
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the Molecular and Materials design program (MMD Hub ) of the Faculty of Science at UvA. What are you going to do? The aim of the project is to use advanced Machine Learning techniques to predict the anharmonic
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to build predictive models Collaborate closely with experimentalists and modelling experts Project Environment This position is part of a collaborative research project involving: Two PhD students at TU
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Immunogenic Peptide Prediction; deployment and optimisation of HADDOCK on Indian HPC Cloud resources. You will actively engage with both the European and Indian research teams, with opportunities
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, and develop methods to optimize cross-modal representations for enhanced predictive modeling. As a postdoctoral researcher, you will: Design and implement novel multimodal AI architectures
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fundamental and applied interest. Specifically, you will develop and/or apply algorithms to predict structures/functions of metabolites and associated biosynthetic gene clusters, as well as to predict
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entitled “Unbiased Drug Target Prediction Reveals Sensitivity to Ferroptosis Inducers, HDAC and RTK Inhibitors in Melanoma Subtypes” (Pla I, Szabolcs BL, Péter PN, Ujfaludi Z, Kim Y, Horvatovich P, et al
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design of these molecules. The goal of this project is to develop ML models for: 1) predicting properties of oligopeptide materials based on peptide sequence and end-group functionalization; 2) guiding