Associate Scientist, Postdoctoral Fellow - Pharmacokinetics

Job Description

Be a part of the legacy: Postdoctoral Research Fellow Program

Our Research Laboratories’ Postdoctoral Research Fellow Program aims to be a best-in-industry program for industrial postdoctoral researchers, designed to provide you with an academic focus in a commercial environment. With the resources, reach, and expertise of a large pharmaceutical company, postdoctoral researchers will be positioned to excel in an institution committed to breakthrough innovation in research and discovery.
 

The Pharmacokinetics, Dynamics, Metabolism, and Bioanalytics (PDMB) function at research laboratories is seeking a highly motivated postdoctoral fellow with expertise in machine learning to help transform drug discovery and preclinical development. You will join an interdisciplinary team and collaborate closely with research partners across our global organization. You will invent, prototype, and apply advanced Machine Learning (ML) methods—particularly in generative modeling and related areas—to expand our capabilities in designing, prioritizing, and characterizing novel therapeutic candidates.

Primary Responsibilities

• Conduct original research to develop state-of-the-art AI/Machine learning methods for drug discovery (e.g., molecular generative models, multi-objective optimization, property prediction, active learning, document authoring, document generation, hybrid AI system, multi-agent system)
• Design and execute experiments, analyze results rigorously, and iterate rapidly on model architectures and training strategies
• Build robust, reproducible code and workflows; contribute to shared libraries and documentation
• Collaborate with chemists, biologists, Pharmacokinetics, Dynamics, Metabolism, and Bioanalytics (PDMB) scientists, and data/ML engineers to translate methods into impactful applications
• Communicate findings through internal presentations and peer-reviewed publications; present at conferences and workshops

Education Minimum Requirements

• Ph.D. (or completion within six months) in Computer Science, Statistics, Physics, Applied Mathematics, Bioinformatics, Computational Biology, Chem/Informatics, Engineering, or a related field

Required Experience and Skills

• Demonstrated research excellence and problem-solving ability; strong motivation to learn, innovate, and deliver
• Proficiency in core ML/statistics topics such as probability, statistical inference, optimization, discrete math/algorithms, and/or probabilistic modeling
• Strong programming skills in Python and experience with modern ML frameworks (e.g., PyTorch, TensorFlow)
• Track record of publications and/or presentations in ML, computational chemistry/biology, or related fields
• Excellent collaboration and communication skills; proven ability to work in cross-functional teams

Preferred Experience and Skills (nice to have)

• Experience with molecular representations (e.g., SMILES, graphs), generative models (e.g., diffusion models, VAEs, flow models), and sequence/structure models (e.g., transformers, GNNs, protein or RNA models)
• Familiarity with cheminformatics/biophysics toolkits (e.g., RDKit), docking or molecular simulation, ADMET modeling, or DMPK-relevant endpoints
• Practical experience with experimental design, active learning, uncertainty quantification, or multi-objective optimization
• Software engineering best practices (Git, testing, containers), and experience working with large datasets and cloud/GPU environment postdoctoralopportunities

Required Skills:

Accountability, ADME, Clinical Study Management, Dosage Forms, Drug Metabolism, Ethical Compliance, Innovation, Machine Learning (ML), Modeling Simulations, Personal Initiative, Pharmaceutical Analysis, Pharmacodynamics, Pharmacognosy, Pharmacokinetic Modeling, Pharmacokinetics, Protocol Development, Regulatory Submissions, Scientific Writing

Preferred Skills:

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