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Postdoctoral Positions for Computational Genomics, Cancer Genetics, and Translational Cancer Biology
Postdoctoral Positions: Computational Genomics · AI-Driven Precision Oncology · Translational Cancer Biology Wang Laboratory, UPMC Hillman Cancer Center Department of Pathology and Human Genetics
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computational approaches for high-dimensional data analysis. https://www.epelmanlab.com/ http://www.uhnresearch.ca/researcher/slava-epelman @EpelmanLab This role has direct mentorship and guidance in grant
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Associate or Senior Editor, BMC Digital Health, BMC Artificial Intelligence, BMC Biomedical Engineer
within relevant fields. Experience, Skills & Qualifications: Essential Educated to PhD or MD level (or equivalent) in artificial intelligence, computer science, biomedical engineering, digital health, data
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to the success of the whole institution. At the Faculty of Computer Science, Institute of Computer Engineering, the Chair of Compiler Construction offers two full-time project positions in the context
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, in vitro models, and large collections of well-annotated clinical specimens. We employ state-of-the-art computational biology/bioinformatics approaches to dissect acute and adaptive responses to RAS
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environment Access to and training in key advanced technologies, for example imaging and in vivo modelling A mentor enabling scheme to aid personal and professional development A rich programme of scientific
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, and gene expression, working at the interface of structural and computational biology, chemistry, and biology. IRB Barcelona is embedded in a vibrant scientific ecosystem and maintains close
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, biomedical, medical, natural sciences, mathematics or computer science), followed by; Master’s degree or equivalent experience in molecular biology, cell biology, biochemistry, biomedicine, medicine, obtained
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in several areas of theoretical and computational physics including, but not limited to, statistical physics (including AI related physics) and large-scale simulation of quantum many-body systems
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. The candidate will lead computational analyses of these datasets, using the laboratory’s suite of existing AI/ML tools to assign structures to unidentified peaks in metabolomic datasets (e.g., https