39 computer-programmer-"https:"-"Inserm" "https:" "https:" "https:" "https:" "J" "J" Fellowship positions at Nature Careers in United States
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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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Master's student, and several Undergrad students, and we plan to recruit an additional Postdoc and a graduate student over the next couple of years. More information about the Kim lab can be found here: http
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the following areas: Developing novel and widely used human genomic transgenic mouse models of HD (e.g. Gu, Neuron , 2022, PMID: 35114102; Gray, J. Neurosci., 2008, PMID: 18550760); Characterizing transcriptomic
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Postdoctoral Fellows to join a dynamic, fully funded research program investigating how environmental exposures and inflammation drive hematopoietic stem cell dysfunction, clonal hematopoiesis, and the
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Bioinformatics Core and Office of AI Research, with opportunities for all fellows to develop their own computational skills). Experimental validation – evaluating RNA drug candidates using cell-based assays
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cells 4. Roles of retinoic acid and its receptor in regulation of the immune system, focusing on T and DC regulation 5. Immunotherapies and chimeric antigen receptor (CAR) therapies Lab website: https
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foundation in related fields who are eager to learn and contribute to our research program. Technical duties include but are not limited to: Lead independent research projects, including hypothesis development
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these discoveries into new therapeutic strategies. Individual projects will be tailored to the candidate’s background and interests, with additional opportunities for collaborative work with the Cancer Program at
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such as system usability, improved patient outcomes, and workflow optimization. Preference will be given to candidates who have completed a clinical informatics fellowship or similar advanced degree program
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(http://vanallenlab.dana-farber.org/) to work on the analysis of new datasets generated in the context of multiple clinically oriented cancer sequencing projects in order help advance efforts