美国辉瑞公司计算机科学、统计学或相关技术领域博士后招聘

　　美国辉瑞公司计算机科学、统计学或相关技术领域博士后招聘

　　The Simulation & Modeling Sciences (SMS) team seeks a postdoctoral researcher interested in developing machine learning methods for single-cell transcriptomics. We are looking for applicants with a demonstrated research background in machine learning, familiarity with biology, 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 transcriptomic 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, active 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 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, Statistics, or related technical field.

　　Undergraduate-level biology or chemistry coursework.

　　Publications and presentations at conferences or workshops such as: NeurIPS, ICML, ICLR, UAI, AISTATS, CVPR, ICCV, ECCV etc.

　　Programming experience in Python and/or C++.

　　Experience with one or more of the following: PyTorch, TensorFlow, JAX, MXNet.

　　PREFERRED QUALIFICATIONS

　　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.