Computer Vision Scientist

Tempus Chicago , IL 60602

Posted 2 weeks ago

Here at Tempus we believe the greatest promise for the detection and treatment of cancer and other diseases lies in building a deep understanding of the interaction between molecular and imaging attributes and clinical treatment.

We're on a mission to redefine how genomic and imaging data are used in a clinical setting. We are looking for Computer Vision Scientist who are passionate about the prospect of building the most advanced data platform in precision medicine.

What You'll Do

  • Research and development of novel imaging data based machine learning algorithms for the product platform

  • Apply statistical and machine learning methods to analyze large, complex data sets

  • Communicate highly technical results and methods clearly

  • Interact cross-functionally with a wide variety of people and teams

Qualifications

  • PhD degree in a quantitative discipline (e.g. statistics, statistical genetics, imaging science, computational biology, computer science, applied mathematics, applied physics or similar) or equivalent practical experience

  • Experience developing, training, and evaluating deep-learning models using public deep learning frameworks (e.g. PyTorch, TensorFlow, and Keras)

  • Experience developing, training, and evaluating classical machine learning models, such as linear and logistic regression, SVMs, Random Forests, and Gradient Boosting

  • Familiar with CUDA and GPU computing

  • Knowledge of different medical imaging modalities, DICOM formats, and/or pathology images

  • Self-driven and work well in an interdisciplinary team with minimal direction

  • Thrive in a fast-paced environment and willing to shift priorities seamlessly

Nice to Haves

  • Kaggle.com competitions and/or kernels track record

  • Experience with AWS architecture

  • Experience working with survival analysis, clinical and/or genomic data

  • Experience working with Docker containers and cloud-based compute environments.

  • Familiarity with neural network techniques (batch-norm, residual connections, inception modules, etc).

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