Job Description:
The Systems Biology Group at the School of Computer Science at Carnegie Mellon University (www.sb.cs.cmu.edu) is inviting applications for a postdoctoral researcher in computational and systems biology. Exceptional candidates may also be considered for a Lane Fellowship in our group (http://cbd.cmu.edu/people/fellows/index.html). Our group focuses on modeling regulatory and signaling networks, the analysis of large scale perturbation and genomic datasets and on biological distributed computing . We develop and use computational methods utilizing techniques from machine learning, statistics and combinatorics in order to solve important biological questions. We are extensively collaborating with multiple experimental groups, in many cases leading to important new insights regarding biological systems and processes.
The open positions are in two major lines of research:
1. Reconstructing regulatory and signaling networks in development and disease response: We are interested in developing and further extending methods for reconstructing dynamic biological networks so that they can integrate new types of epigenetic and single cell data, can lead to detailed spatio / temporal models and hypotheses and recover pathways involved in drug and disease response. These methods combine machine learning approaches (graphical models, classification, clustering, active learning) with combinatorial and graph based algorithms (Integer programming, edge orientation in graphs, constraint stratification problems) and will be applied to study lung development, HIV response and to reconstruct pathways based on LINCS perturbation data.
2. Biological distributed computing: Biological systems are distributed and in most cases operate without central control. Such systems must solve information processing problems that are often very similar to problems faced by computational systems, including coordinated decision making, routing and navigation and more. We are working on several projects that rely on the similarity between biological and computational systems to improve our understanding of complex biological processes. These include studies of neural network development, plasticity and utilization and studies of bacterial communication and coordination. In both projects we are developing advanced computational methods (including belief propagation, distributed computing models etc.) and work closely with experimentalists to test and validate the resulting models.
Our group has a long tradition of successful placement for PhD students and postdocs and several past members have gone to become faculty members and PIs in leading universities in the US and Europe. For these positions we are looking for candidates with a Ph.D. degree in computer science, statistics, bioinformatics, or related quantitative fields and with publications in top conferences or journals in relevant areas of research.
Job Information
Position Type: Post-Doctoral Research Fellowships
Start Date: January 01, 2015
Duration: Full Time
Status: open
Additional Info:
[Click Here to Access the Original Job Post]
The Systems Biology Group at the School of Computer Science at Carnegie Mellon University (www.sb.cs.cmu.edu) is inviting applications for a postdoctoral researcher in computational and systems biology. Exceptional candidates may also be considered for a Lane Fellowship in our group (http://cbd.cmu.edu/people/fellows/index.html). Our group focuses on modeling regulatory and signaling networks, the analysis of large scale perturbation and genomic datasets and on biological distributed computing . We develop and use computational methods utilizing techniques from machine learning, statistics and combinatorics in order to solve important biological questions. We are extensively collaborating with multiple experimental groups, in many cases leading to important new insights regarding biological systems and processes.
The open positions are in two major lines of research:
1. Reconstructing regulatory and signaling networks in development and disease response: We are interested in developing and further extending methods for reconstructing dynamic biological networks so that they can integrate new types of epigenetic and single cell data, can lead to detailed spatio / temporal models and hypotheses and recover pathways involved in drug and disease response. These methods combine machine learning approaches (graphical models, classification, clustering, active learning) with combinatorial and graph based algorithms (Integer programming, edge orientation in graphs, constraint stratification problems) and will be applied to study lung development, HIV response and to reconstruct pathways based on LINCS perturbation data.
2. Biological distributed computing: Biological systems are distributed and in most cases operate without central control. Such systems must solve information processing problems that are often very similar to problems faced by computational systems, including coordinated decision making, routing and navigation and more. We are working on several projects that rely on the similarity between biological and computational systems to improve our understanding of complex biological processes. These include studies of neural network development, plasticity and utilization and studies of bacterial communication and coordination. In both projects we are developing advanced computational methods (including belief propagation, distributed computing models etc.) and work closely with experimentalists to test and validate the resulting models.
Our group has a long tradition of successful placement for PhD students and postdocs and several past members have gone to become faculty members and PIs in leading universities in the US and Europe. For these positions we are looking for candidates with a Ph.D. degree in computer science, statistics, bioinformatics, or related quantitative fields and with publications in top conferences or journals in relevant areas of research.
Job Information
Position Type: Post-Doctoral Research Fellowships
Start Date: January 01, 2015
Duration: Full Time
Status: open
Skills :
Areas :
Additional Info:
[Click Here to Access the Original Job Post]