Pfizer Postdoctoral Research Fellow in Cambridge, Massachusetts
The Simulation & Modeling Sciences (SMS) team seeks a postdoctoral researcher interested in research at the intersection of bioinformatics and machine learning, with a focus on developing methods for single-cell transcriptomics. We are looking for applicants with a demonstrated research background in machine learning, familiarity with computational biology/bioinformatics, and the ability to translate ideas from theory to practical applications.
Our group is comprised of experts from across computational, chemical, and biological sciences, providing an environment for multidisciplinary research with access to rich data sources across Pfizer's R&D organization. Additionally, we have close links to top academic institutions around the world as well as with internal partners and research units. The post-doctoral researcher will primarily focus on areas within machine learning-including generative models-but should be broadly interested in other approaches that can be leveraged to enable impactful analyses and modeling of single-cell transcriptomic data.
Participate in cutting-edge research in machine learning leveraging Pfizer's in-house data and compute infrastructure. Specific subfields of research may include generative models, active learning, meta-learning, and reinforcement learning.
Write well-documented, tested code as part of Pfizer's stack within a high-performance scientific computing environment.
Work closely with other groups within SMS as well as other partners across R&D to develop algorithms and models.
Effectively communicate the value and efficacy of new methods to technically diverse internal audiences.
Write and publish articles in top peer-reviewed journals in the field and deliver scientific and technical presentations at internal and external venues.
Ph.D. in Computer Science, Statistics, Bioinformatics, Computational Biology, Applied Math, or related technical field.
Undergraduate-level biology and/or chemistry coursework.
Publications and presentations at conferences or workshops
Programming experience in Python and one or more of the following: C++, Lua, Java, Scala.
Experience with one or more of the following: PyTorch, TensorFlow, JAX, Caffe, Pyro.
Research experience in developing machine learning methods for life science applications in industry or academia.
Advanced coursework in biology or chemistry.
Programming experience with GPUs/CUDA.
Strong portfolio of open-source software available on GitHub, GitLab, etc.
Other Job Details:
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