Merck is a leading pharmaceutical company seeking a highly motivated postdoctoral fellow with expertise in machine learning to help transform drug discovery and preclinical development. The role involves conducting original research, collaborating with interdisciplinary teams, and applying advanced machine learning methods to develop novel therapeutic candidates.
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
Skills
- Ph.D. (or completion within six months) in Computer Science, Statistics, Physics, Applied Mathematics, Bioinformatics, Computational Biology, Chem/Informatics, Engineering, or a related field
- 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
- 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
Benefits
- Medical
- Dental
- Vision healthcare and other insurance benefits (for employee and family)
- Retirement benefits, including 401(k)
- Paid holidays
- Vacation
- Compassionate and sick days
Company Overview
Apply Now