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leverage state of the art machine learning models (AlphaFold2, RFdiffusion) and multi-omics data integration to guide the rational design and optimization of therapeutic antibodies. Overall, you will have
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to study chromatin and gene regulation in mammalian cells and human disease systems. Current ongoing projects include: statistical modeling and advanced machine learning/AI method development for predicting
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strengths of the University of Tübingen in Computer Sciences and Machine Learning. Potential research directions include, but are not limited to, phylogenetic, demographic, ecological and biogeographic
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, scalability, and effective performance across university use cases. Develops, trains, and fine-tunes machine learning models for a variety of university applications. Conducts experiments to evaluate model
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of interest include, but are not limited to, stochastic, discrete, large-scale, and data-driven optimization, machine learning methods for sequential decision making, or stochastic modeling and prescriptive
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statistical modeling, machine learning, data analysis, and reporting Proficiency in Python or R Ability to plan, execute and control a project, establishing realistic estimates and reporting timelines Advanced
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3 Mar 2026 Job Information Organisation/Company UNIVERSITE LE HAVRE NORMANDIE Research Field Computer science » Computer systems Computer science » Database management Computer science » Modelling
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development of specialised APIs and lifecycle management of the machine learning model. This role will require a strong understanding of electrical engineering and power systems principles in order to encourage
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extraction. 2. Be responsible for the application of AI and machine learning techniques to improve tissue image interpretation, for use in case selection and tissue annotation for tissue microarray
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AI. Candidates must possess substantial experience in artificial intelligence and machine learning methods, specifically in AI-driven materials discovery, machine learning applications for materials