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Experience1 - 4 Research FieldPharmacological sciencesYears of Research Experience1 - 4 Additional Information Eligibility criteria # Education and experience - PhD in analytical chemistry, biochemistry
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on developing deep learning methods for the reconstruction and physical analysis of ATLAS experiment data. The selected candidate will develop innovative analysis methods for the reconstruction and physical
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settings with limited data and transfer learning across species from human to mouse, and beyond. • Apply explainability methods to extract biological insight from trained models. • Keep up to date with
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FieldMathematicsYears of Research Experience1 - 4 Additional Information Eligibility criteria • PhD in condensed matter physics, materials science, or a related field; • Strong knowledge of thin-film growth techniques
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, Kupffer cells) • Characterise the reconstructed tissue: biologically, mechanically and functionally, using the laboratory's various methods or other specialist expertise. • Study the response of the liver
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Additional Information Eligibility criteria Knowledge PhD in Biomaterials, Biomedical Engineering, Bioengineering, Materials Science or a closely related field. Strong background in biomaterials, tissue
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remains largely unexplored and represents a fascinating frontier for molecular computing. The development of new methods will be necessary to enable the manipulation of data within complex molecular
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-temporal context and their environment. ECO-EVO BIODIV draws on a wide range of conceptual frameworks, approaches and methods from an interdisciplinary perspective. Within the framework of ECO-EVO BIODIV
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Eligibility criteria Skills: • Strong knowledge of theoretical chemistry methods (DFT, MD, ab initio). • Experience with molecular modeling software (e.g., MOLPRO, DeMon2K). • Interest in interdisciplinary
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, electron microscopy, X-ray diffraction, etc.). Methodology: • Design and synthesize model substrates to measure the activity of enzyme mimics. • Develop analytical methods to detect and quantify chemical