We are inviting an independent and highly motivated researcher with a strong quantitative scientific background for a computational postdoctoral fellow position. The successful candidate will be responsible for developing novel analytics for analyzing, visualizing, and interpreting single cell omics data to understand tumor-microenvironment changes, cancer patient treatment response, and for treatment prediction. There will be ample opportunities to interact directly with basic, translational, clinical researchers, bioinformaticians, and biostatisticians to develop algorithms or tools for analyzing single cell data.
The ideal candidate is a highly motivated researcher with a strong quantitative scientific background and programming skills. Ability to learn new tools and knowledge is highly desirable.
Required: PhD in Data Science, Bioinformatics, Biostatistics, Computer Science, or related fields
Preferred:
• Strong programming skills in languages such as R, Matlab, Python, and JavaScript, etc.
• Experience in developing open-source software and/or interactive web-based tools.
• Solid quantitative background and experiences of developing novel algorithms or methods for analyzing time series data.
• Experience of integration of multi-omics data including whole exome sequencing, RNA-seq, metabolomics, proteomics, etc.
• Research background in machine learning and AI
• Experience with cloud computing/compute cluster environments
• Excellent communication and writing skills.
Please email a cover letter, CV, and contact information of 3 references.
For more information, please check: labpages2.moffitt.org/chen/