Post Doctoral Fellow- Drug Discovery AI/Machine Learning - Hybrid
- 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)
- 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
- 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 environments
Merck & Co., Inc., Rahway, NJ, USA, also known as Merck Sharp & Dohme LLC, Rahway, NJ, USA, does not accept unsolicited assistance from search firms for employment opportunities. All CVs / resumes submitted by search firms to any employee at our company without a valid written search agreement in place for this position will be deemed the sole property of our company. -No fee will be paid in the event a candidate is hired by our company as a result of an agency referral where no pre-existing agreement is in place. Where agency agreements are in place, introductions are position specific. Please, no phone calls or emails.- Employee Status:
Regular Relocation:
Domestic VISA Sponsorship:
Yes Travel Requirements:
10% Flexible Work Arrangements:
Hybrid Shift:
Not Indicated Valid Driving License:
No Hazardous Material(s):
N/A Required Skills:
Algorithms, Artificial Intelligence (AI), Computational Biology, Computational Chemistry, Computer Science, Electrical Engineering, Machine Learning, Physics, Statistical Inferences, Statistics -Preferred Skills: Job Posting End Date:
10/7/2025
*A job posting is effective until 11:59:59PM on the day BEFORE -the listed job posting end date. Please ensure you apply to a job posting no later than the day BEFORE the job posting end date. Requisition ID: R366327
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