H. Lee Moffitt Cancer Center, Strategic Workforce Management

Position ID: 1607-POSTDOC4 [#13987, 33020 & 32942]
Position Title: Postdoctoral Fellow in Mathematical Modeling
Position Type: Postdoctoral
Position Location: Tampa, Florida 33612, United States [map]
Application Deadline: (posted 2019/06/04, listed until 2019/12/03)
Position Description:    

*** The account for Strategic Workforce Management, H. Lee Moffitt Cancer Center has expired. ***

The laboratory of Dr. Noemi Andor seeks two recruit two Postdoc Fellows to drive Moffitt’s mission in contributing to the prevention and cure of cancer. Both opportunities are projects part of our NIH-awarded K99/R00. Postdoctoral Fellow in Mathematical Modeling – Req# 33020 Sequencing technologies have evolved to give us an unprecedented resolution on a tumor’s clonal constitution. Yet what we see is only one, or at most a few snapshots in time. Inferring from this data, what factors govern the observed clonal composition and how it will change in the context of our interventions or the absence thereof, is a daunting task. We seek applications from individuals with a PhD in applied mathematics or physics to develop mathematical models that explain the extent of consilience among multiple perspectives on a tumor’s clonal constitution.

Position Highlights: • Successful candidate will play a critical role leveraging in-house high-throughput single-cell DNA sequencing datasets, in conjunction with H&E imaging data, to generate testable hypotheses that guide future wetlab experiments. • This person will be part of a cross-functional effort to inform therapeutic interventions that steer clonal interference into any therapeutically desirable direction.

The Ideal Candidate: • Experience in designing testable mathematical models to evaluate competitive assumptions about reality is required. Ideal candidate has worked extensively with both, top-down (ODEs, PDEs, adaptive dynamics) and bottom-up approaches (Potts model, lattice-gas CAs, off-lattice) and has experience in choosing the appropriate method for the task at hand. Strong quantitative skills and familiarity with statistical model selection techniques, such as Approximate Bayesian computation are also necessary.

Responsibilities: • Successful candidate will work closely with machine learning specialist to perturb ecological balance and interfere with cycling fluctuations in the composition of a metapopulation.

Postdoctoral Fellow in Machine Learning – Req# 32942 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.

Position Highlights: • 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.

Responsibilities: • 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.

How to Apply: Please send your CV and cover letter to Dr. Noemi Andor at nandor@stanford.edu and make note which opportunity/req # you are apply to.


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Contact: Dr. Noemi Andor
Email:
Postal Mail:
12902 Magnolia Drive
Tamp, FL 33612
Web Page: https://www.moffitt.org/

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