Northeastern University's Institute for Experiential AI is looking to solve challenges and opportunities in human-machine collaboration by developing AI-driven technologies. This role specifically focuses on applying AI to biological data for drug discovery and phenotype prediction.
Requirements
- demonstrable experience in building AI models for alternative splicing
- 1+ years of experience in bioinformatics and/or biological data formats
- familiarity with RNA Biology (alternative splicing) and genomics
- experience in research software development, FAIR data/open science, life sciences data systems
- strong demonstrable background in machine learning
- applications of neural networks to the analysis of multi-omics data
- models for predicting phenotypes using genotype data, biological data integration
Responsibilities
- develop AI algorithms for drug discovery with a combination of public and proprietary data
- develop AI algorithms for variant effect prediction with models of alternative splicing
- scientific programming
- data analysis
- prepare work for submission to journal/conference publications
- conduct applied research
- analysis of various kinds of ‘omics data (e.g., metabolomics, proteomics, genomics, transcriptomics, etc.)
Other
- This is a 1-year fixed-term position, eligible for renewal based on grant funding.
- The ML Performance Engineer will be based out of the Boston campus, MA
- mentoring graduate students
- work with graduate students working on the same project
- collaborate with academic and industry partners of EAI
