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component disciplines; in explainable multi-modal deep learning models, in causal statistical models and in human-machine teaming and AI ethics. The researcher will conduct internationally-leading research in
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inference, and Machine Learning methods. In addition to leading their own research projects, the appointed candidate will have the opportunity to contribute to the projects of PhD students in the group, as
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for soft materials, with particular emphasis on thermo–visco–hyperelastic behavior, integrating continuum mechanics, scientific machine learning (SciML), and computational physics. The project aims
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automates building and modifying surface structures, submitting DFT calculations, post-processing electronic structure and vacancy energies, and extracting machine-learning descriptors for modeling oxygen
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. Start date is 1 March 2026 or as soon as possible thereafter. The project is about developing machine learning (ML) methods that help to develop the food of the future. The successful candidate is
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AI systems and interpretable machine learning, System integration implementation, Test environment configuration, Validation and stress testing, Deployment and configuration in test environments
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-omics liquid biopsy data for minimal residual disease (MRD) detection, quantification, and assessment. This project will involve applying and evaluating statistical and machine learning models for data
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skills with experience in cloud data warehouse systems such as Snowflake and data preparation tools like Tableau Prep Experience with predictive modeling, machine learning, AI applications, and advanced
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, dimensionality reduction and/or machine learning methods (e.g., Lasso, ridge regression) is highly desirable. Familiarity with neurostimulation, Parkinson’s disease, or neuropsychological assessment tools is