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. Expertise and knowledge in AI deep learning model development on histology whole slide imaging analysis in computational pathology is essential. Applicants should have a solid publication record and
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, transcriptomics data, Nanopore long read sequencing analysis and/or multimodality deep learning model development in different sarcomas. Publications in related fields will be a strong advantage. Opportunities for
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on complex problems involving the development of new theories and methodologies. The research will be largely focused on the development of predictive computational tools for the analysis of the spatial spread
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Apply online now: https://karriere.klinikum.uni-heidelberg.de/index.php?ac=application&jobad_id=27582 Postdoc - Molecular Biology Research in MLLr acute leukemia (f/m/d) Stellenanzeige merken
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lead advanced statistical analyses integrating ecological datasets with spatiotemporal modelling frameworks. The work will contribute with evidence-based data to development of ecosystem-based
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organoid development, under the supervision of Professor Jade L. L. Teng. For inquiries, please contact Professor Teng at llteng@hku.hk . Further information about the Faculty can be found at https
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 7 days ago
: 265566 Vacancy ID: PDS003971 Position Summary/Description: The Miao Lab in the Computational Medicine Program and Department of Pharmacology at the University of North Carolina – Chapel Hill (https
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the new Ashley School of the Environment and Global Development. This section is multidisciplinary with emphases in applied ecology, human dimensions of natural resource management, policy/governance
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humanities, and the future of archives. With funding from the Mellon Foundation, HBW will redesign and expand the Black Literature Network website’s archives portal (https://www.blacklitnetwork.org/archives
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candidate will lead advanced statistical analyses integrating ecological datasets with spatiotemporal modelling frameworks. The work will contribute with evidence-based data to development of ecosystem-based