The Simulation & Modeling Sciences (SMS) team seeks a postdoctoral researcher interested in developing machine learning methods for mass spectrometry-based proteomics. We are looking for applicants with a demonstrated research background in machine learning, familiarity with biology and/or chemistry, and the ability to translate ideas from theory to practical applications.

Our group sits at the intersection of computational, chemical, and biological sciences, providing an environment for multidisciplinary, applied research with access to heterogeneous 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 deep generative models—but should be broadly interested in other approaches that can be leveraged to enable impactful analyses and modeling of mass spectrometry-based proteomics data.

ROLE RESPONSIBILITIES

• 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, deep learning, meta-learning, and reinforcement learning. 
• Write well-documented, tested, modular code, individually and collaboratively, atop Pfizer’s Python/C++ technical stack within a high-performance scientific computing/cloud 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. 

BASIC QUALIFICATIONS

• Ph.D. in Computer Science, Computational Biology, Statistics, or related technical field.
• Undergraduate-level biology or chemistry coursework.
• Publications and presentations at conferences or workshops in computational field.
• Programming experience in Python and/or R.
• Experience with one or more of the following: PyTorch, TensorFlow, JAX, Theano, Caffe.

PREFERRED QUALIFICATIONS

• Research experience in applying machine learning to biology in industry or academia
• Demonstrated experience in handling proteomics and/or other mass spectrometry-based datasets
• Programming experience with GPUs
• Strong portfolio of open-source software