DATA SCIENTIST & PLATFORM DEVELOPMENT INTERNS

Lantern Pharma is seeking a select group of talented and highly motivated Data scientists and Platform Development interns to build and enhance our drug rescue and repositioning Artificial Intelligence (AI) platform that can predict drug response, therapeutic benefit, and survival outcome of cancer patients, and generate biomarkers for specific cancer types for a drug candidate of interest.

This is a graduate or post-graduate level paid internship position with flexible hours and could potentially lead to a full-time position. The ideal future team member must possess a passion to solve real-world problems in cancer treatment by leveraging leading-edge Artificial Intelligence and Machine Learning techniques with involvement in optimizing our core bioinformatics, computational, and AI algorithms to improve their level of robustness, effectiveness, efficiency, and performance. Depending on the level of experience and technical knowledge interns may also participate in the development of our data infrastructure and data models and in the development of data dashboards.

RESPONSIBILITIES: ● Searching, querying, curating, and processing information from preclinical and clinical databases and tools related to cancer drug and biomarker development ● Conducting machine learning analyses to identify predictive gene signatures from multi-omics (transcriptomics, mutation, methylation, protein expression, etc.) data ● Introducing industry-leading or academic-based ideas and research to solve complex questions in cancer biology and drug development ● Designing infrastructure that powers Lantern´s AI platform - RADR ● Generating insightful visualizations for scientific publications and posters

BASIC QUALIFICATIONS: ● Ph.D. candidate, graduate, post-graduate, or equivalent research experience in a relevant, quantitative field such as computer science (AI or ML emphasis), computational biology, bioinformatics, statistics, applied math, bioengineering with cancer biology/ genomics focus ● Experience in one or more scripting languages like R, Perl, and Python is required ● Knowledge of ML algorithms: how they work, their proper use cases, their limitations, etc. ● Expertise, demonstrated by research publications or industrial experience, in applied machine learning, data mining, pattern recognition, or AI ● Strong knowledge of mathematical fundamentals: statistics, probability theory, linear algebra. ● Practical and theoretical understanding of fundamental models and algorithms in supervised and unsupervised learning ● Excellent written and verbal communication skills, working in a fast-paced team environment