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of shape modelling and contribute to attracting external funding. You will assist in the supervision of PhD students. Profile You hold a PhD in physics, mathematics, engineering or computer science from a
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with a minimum of one year eligibility remaining. •Strong expertise in first-principles modeling or molecular simulations relevant to catalysis, and experience with machine learning models. •Deep
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annotation, and emerging machine-learning and generative methods for spectra or structure proposals. Evaluate and test emerging technologies (hardware and software) in close interaction with collaborators and
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controls, using deep learning and explainable AI Communicate and discuss results with stakeholders to integrate the findings into water management practices Interpret, publish and present findings in peer
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the dispersion of these macroplastic items in these flow fields; comparing the results of the simulations to results from an experimental campaign of floating trackers; collaborating with a postdoc and two PhD
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signal-to noise Post-processing: denoising, reconstruction algorithms Comparison with high-field MRI: deep-learning and other AI modalities for low-field MRI optimization Close cooperation with
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industrial applications. Contribute to the development of scalable and interpretable AI tools for real-world deployment. Qualifications: A PhD in Computer Science, Machine Learning, NLP, or a related field
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Research, or a related field. Solid research background and practical experience in one or more of the following areas: Reinforcement Learning / Deep Reinforcement Learning Fine-tuning and Application
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experience in NGS data analysis and genomics pipelines since similar to proteomic pipelines. Record of prior first-author publications. A deep interest in biomarker research and discovery. The drive to pursue
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, and MRV performance) and identify optimal deployment models coupled with learnings from forest management. Conduct techno-economic and life-cycle assessments (TEA/LCA) integrating forest operations