Career oppertunity: Modeling & simulation position at Merrimack Pharmaceuticals

Job Description : Merrimack is looking for a highly-motivated computational biologist with a background in PK modeling and a strong interest in Systems Biology to join our efforts by collaborating in the development of mechanistic biochemical pathway models and linking them to PK models. Candidates with a strong background in cancer biology, PK modeling and biochemical pathway modeling are preferred. The individual will be part of an interdisciplinary group that applies systems biology to patient enrichment and drug development in oncology. The successful candidate shows an interest in new technologies, quantitative biology and in applying mathematical models to cancer research. We are looking for an individual who is flexible, wants to change the way drug discovery is traditionally done and has a highly innovative mindset. Some of the main responsibilities include collaboration in the development of the company's mathematical models and use of software-engineering techniques to develop and maintain the quality of models, associated software, and data. Also, the successful candidate will regularly document work in our electronic lab notebook system, present the work at regular company meetings, and prepare graphs, figures, and tables of data for presentation. Job Requirements : Successful candidates are likely to have a PhD in chemical or biomedical engineering, computational biology, biophysics, computer science, or applied mathematics. A master's degree with appropriate experience might also be suitable. The candidate must have tackled medium- to large-scale mathematical models of biological phenomena (e.g., signaling pathways, transcriptional regulatory networks, biophysical simulations), and ideally linked these to PK models. We also require knowledge of numerical solution of ODEs and multi-parameter optimization. Some familiarity with, and interest in further understanding, the concepts of molecular biology as applied to transcriptional and post-translational regulatory networks and their disregulation in cancer will be required. Most importantly, successful candidates must be able to engage colleagues in cross-disciplinary scientific discussion, write about own work, and collaborate within and across multidisciplinary teams. They will be flexible and results-oriented. Must be able to quickly formulate and test ideas in a scripting language (e.g., MATLAB, Python). Knowledge of statistical distributions and hypothesis testing, machine-learning algorithms (e.g., PLSR, support-vector machines, decision trees) would be useful, though not essential. Some familiarity with software engineering tools (e.g., CVS, Subversion, unit tests) and parallel/distributed computing environments would be a plus. If you are interested, please visit: https://merrimackpharma.tms.hrdepartment.com/cgi-bin/a/highlightjob.cgi?jobid&lcid=en-US