Weill Cornell Medical College of Cornell University and Department of Computer Science at Hunter College, The City University of New York are seeking a joint Postdoctoral Research Scientist in the fields of machine learning, computational genomics, translational bioinformatics, systems biology, and biophysics. The candidate is expected to develop new computational methods for the multi-scale modeling of drug actions and genotype-phenotype associations as well as apply state-of-the-art computational tools to omics data analysis and integration, drug discovery and precision medicine. Depending on candidate’s interests and backgrounds, specific projects may include but not limited to: predicting genome-wide protein-ligand interactions, integrating and analyzing multiple omics data for drug target identification, identifying individualized drug response biomarkers, text mining of electronic medical records, knowledge representation and ontology development of drug responses, drug repurposing, and any other open questions in systems pharmacology. The Postdoctoral Scientist has the opportunity on developing or enhancing skills in statistics, machine learning, and big data analytics. Our multiple NIH funded projects involve close collaborations between Cornell University, University of Virginia, and CUNY. We provide you with the ideal research environment to advance your career: freedom to pursue your interests, interaction across disciplines, and excellent salary.
Minimum requirements: Candidates must have a Ph.D. in bioinformatics, computational biology, statistics, mathematics, biophysics, computer science, and/or related fields. The candidate also must have a strong background and experience in one or more of the following areas: genomics, gene expression data analysis, systems biology, chemoinformatics, biophysics, machine learning, or multi-variable statistics; strong programming skills using Python or R, and familiarity with computing environments such as MATLAB; effective communication skills; self-motivated and independent.
Preferred qualifications: Expertise and strong publication record in one or more areas related to high-dimensional data mining, multi-view multi-target learning, self-supervised learning, semi-supervised learning, transfer learning, deep learning, or natural language processing.