Tumor evolution proceeds at a rapid pace. Drug development does not. In order for cancer therapy to achieve a durable response over years rather than months, it must “evolve” as quickly as the tumor does. In contrast to a cell’s DNA, its RNA – the transcriptome – is a highly dynamic machinery which operates within ranges, orders of magnitudes wider than those of the genome. Its role of shaping clonal evolution and implementing a cell’s response to its environment is undoubted, yet nowhere close to understood. We seek applications from a computer scientist with a PhD and strong background in machine learning to develop general metrics of cell fitness using supervised machine learning approaches. This project is part of our NIH-awarded K99/R00.
• We develop software solutions that enable data-driven, clinical, decision-making by providing oncologists and specialized tumor-boards with patient-specific genomic information. Executing this mission will rely on a long-term synergy between various evolving perspectives on tumor populations and on an interdisciplinary effort to sharpen these perspectives.
The Ideal Candidate:
• Experience with machine learning approaches, especially supervised methods such as Bayesian MKL, deep neural networks (Bayesian neural nets, LSTM, convolutional) is required. Experience with PyTorch or TensorFlow is also needed. Prior work with very large datasets, such as CIFAR-10 and imaging data pre-processing is preferred but not required. Experience with Java and OO programing concepts is also a plus.
• Successful candidate will work with a powerful combination of single-cell RNA sequencing data and live-cell imaging to learn various aspects about a clone’s fitness from its transcriptome.
• Successful candidate will work closely together with mathematicians towards steering the fitness landscape of coexisting cancer clones in a systematic, directed fashion.
Credentials and Qualifications:
• PhD in computer science or relevant field.
How to Apply: Please send your CV and cover letter to Dr. Noemi Andor at Noemi.Andor@Moffitt.org.