Difference between revisions of "Openings"

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As of November 2019, the Chuang Lab is seeking a postdoctoral scientist or research scientist to join our projects combining computational and experimental approaches to study cancer. Our current projects are at the intersections of cancer biology, machine learning, genomics, image analysis, and pathology. This is a fast-moving field with tremendous opportunities to understand cancer and impact patient outcomes. New team members will have unique opportunities to investigate mechanisms of cancer, to design new studies, and to develop novel artificial intelligence approaches to mine these data. Our lab seeks to understand how tumor ecology and evolution impact susceptibility and response to immunotherapy, targeted therapy, and chemotherapy. We interrogate these questions by generating data from clinical samples and model systems (xenografts, organoids) and by mining large cancer genomic and imaging datasets using techniques such as deep learning and evolutionary modeling. This position is supported by an R01 grant from the NCI awarded through 2023.
 
As of November 2019, the Chuang Lab is seeking a postdoctoral scientist or research scientist to join our projects combining computational and experimental approaches to study cancer. Our current projects are at the intersections of cancer biology, machine learning, genomics, image analysis, and pathology. This is a fast-moving field with tremendous opportunities to understand cancer and impact patient outcomes. New team members will have unique opportunities to investigate mechanisms of cancer, to design new studies, and to develop novel artificial intelligence approaches to mine these data. Our lab seeks to understand how tumor ecology and evolution impact susceptibility and response to immunotherapy, targeted therapy, and chemotherapy. We interrogate these questions by generating data from clinical samples and model systems (xenografts, organoids) and by mining large cancer genomic and imaging datasets using techniques such as deep learning and evolutionary modeling. This position is supported by an R01 grant from the NCI awarded through 2023.
  
While our lab focuses on computational approaches, we also generate our own data by making use of the extensive scientific services at JAX and in collaboration with many experimental colleagues. These projects involve close collaborations with clinical and experimental groups at JAX (Robson, Palucka, Liu, Anczukow-Camarda, et al.) and externally (NCI PDXNet, Yale, Northwestern, UCSD, et al). We have particular access to patient-derived xenografts (PDXs) due to Prof. Chuang's position as co-PI of the National Cancer Institute's PDXNet Consortium Data Commons and Coordination Center, in addition to the extensive data and experimental resources for PDXs at JAX. Thus candidates will have novel opportunities at the intersection of data interpretation, algorithms, and experimental design.'''
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While our lab focuses on computational approaches, we also generate our own data by making use of the extensive scientific services at JAX and in collaboration with many experimental colleagues. These include clinical and experimental groups at JAX (Robson, Palucka, Liu, Anczukow-Camarda, et al.) and with external partners (NCI PDXNet, Yale, Northwestern, UCSD, et al). We have particular access to patient-derived xenografts (PDXs) due to Prof. Chuang's position as co-PI of the National Cancer Institute's PDXNet Consortium Data Commons and Coordination Center, in addition to the extensive data and experimental resources for PDXs at JAX. Thus candidates will have novel opportunities at the intersection of data interpretation, algorithms, and experimental design.'''
  
 
For this position, the ideal candidate will have Ph.D. experience in computational biology, cancer biology, evolutionary biology, digital pathology, or a similar discipline. Outstanding applicants new to computational biology will also be considered as the lab has an extensive record of training candidates who originally studied in other fields, including physics, applied math, computer science, electrical engineering, and experimental molecular biology. A particular area of growth for the lab is the combination of genomic and pathological analysis, and those with a medical background in pathology but a strong interest in computational approaches are encouraged to apply. Computing (programming, data analysis, statistical inference, etc), publication, and communication skills will be essential. Across these backgrounds, enthusiasm and aptitude for computational biology are what define the lab. Interested applicants should send a CV and a research statement to Jeffrey Chuang at jeff.chuang@jax.org. Reference letters will be requested subsequently.
 
For this position, the ideal candidate will have Ph.D. experience in computational biology, cancer biology, evolutionary biology, digital pathology, or a similar discipline. Outstanding applicants new to computational biology will also be considered as the lab has an extensive record of training candidates who originally studied in other fields, including physics, applied math, computer science, electrical engineering, and experimental molecular biology. A particular area of growth for the lab is the combination of genomic and pathological analysis, and those with a medical background in pathology but a strong interest in computational approaches are encouraged to apply. Computing (programming, data analysis, statistical inference, etc), publication, and communication skills will be essential. Across these backgrounds, enthusiasm and aptitude for computational biology are what define the lab. Interested applicants should send a CV and a research statement to Jeffrey Chuang at jeff.chuang@jax.org. Reference letters will be requested subsequently.

Latest revision as of 17:04, 25 November 2019

Computational biology is critical not only to the processing and manipulation of data, but to the scientific discovery process itself. Our lab empowers computational biologists to make discoveries by providing a rich work environment for computational/mathematical analysis of biological systems, access to novel data and computational infrastructure, resources to conduct new experimental measurements, and partnerships with experimental colleagues with shared interests.

As of November 2019, the Chuang Lab is seeking a postdoctoral scientist or research scientist to join our projects combining computational and experimental approaches to study cancer. Our current projects are at the intersections of cancer biology, machine learning, genomics, image analysis, and pathology. This is a fast-moving field with tremendous opportunities to understand cancer and impact patient outcomes. New team members will have unique opportunities to investigate mechanisms of cancer, to design new studies, and to develop novel artificial intelligence approaches to mine these data. Our lab seeks to understand how tumor ecology and evolution impact susceptibility and response to immunotherapy, targeted therapy, and chemotherapy. We interrogate these questions by generating data from clinical samples and model systems (xenografts, organoids) and by mining large cancer genomic and imaging datasets using techniques such as deep learning and evolutionary modeling. This position is supported by an R01 grant from the NCI awarded through 2023.

While our lab focuses on computational approaches, we also generate our own data by making use of the extensive scientific services at JAX and in collaboration with many experimental colleagues. These include clinical and experimental groups at JAX (Robson, Palucka, Liu, Anczukow-Camarda, et al.) and with external partners (NCI PDXNet, Yale, Northwestern, UCSD, et al). We have particular access to patient-derived xenografts (PDXs) due to Prof. Chuang's position as co-PI of the National Cancer Institute's PDXNet Consortium Data Commons and Coordination Center, in addition to the extensive data and experimental resources for PDXs at JAX. Thus candidates will have novel opportunities at the intersection of data interpretation, algorithms, and experimental design.

For this position, the ideal candidate will have Ph.D. experience in computational biology, cancer biology, evolutionary biology, digital pathology, or a similar discipline. Outstanding applicants new to computational biology will also be considered as the lab has an extensive record of training candidates who originally studied in other fields, including physics, applied math, computer science, electrical engineering, and experimental molecular biology. A particular area of growth for the lab is the combination of genomic and pathological analysis, and those with a medical background in pathology but a strong interest in computational approaches are encouraged to apply. Computing (programming, data analysis, statistical inference, etc), publication, and communication skills will be essential. Across these backgrounds, enthusiasm and aptitude for computational biology are what define the lab. Interested applicants should send a CV and a research statement to Jeffrey Chuang at jeff.chuang@jax.org. Reference letters will be requested subsequently.

The Jackson Laboratory for Genomic Medicine (JAX-GM) is part of The Jackson Laboratory, a non-profit research institute whose mission is to discover precise genomic solutions for disease and empower the global biomedical community in the shared quest to improve human health. The JAX-GM campus is in Farmington, CT, adjacent to the University of Connecticut Medical School (UCONN-Health). JAX-GM resides in a state-of-the-art facility constructed in 2014 with cutting edge resources in sequencing, single cell biology, computational biology, cloud computing, imaging, genetic engineering, and xenografting, among other technologies. JAX-GM currently has 28 faculty members with focuses in cancer genomics, computational biology, immunology, microbiome, human genetics, and genome technology development. The Chuang lab collaborates regularly with many labs at JAX-GM and our sister institute, The Jackson Laboratory for Mammalian Genetics (JAX-MG in Bar Harbor, ME) which has been a world leader in mouse genetics for the past century. Among JAX-GM, JAX-MG, and UCONN-Health, there is a vibrant community of more than 100 scientists specialized to computational biology, providing frequent opportunities for interactions and discussions. JAX-GM is in the greater Hartford region, a lively metropolitan area of over one million people that is a two hour drive from both Boston and New York City.

Aside from what is listed above, our lab is always open to new students and postdocs on an ad hoc basis. Please contact the lab for specific questions.