Post Doctoral Fellow- Drug Discovery AI/Machine Learning - Hybrid
- 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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