Post Doctoral Fellow- Drug Discovery AIMachine Learning Hybrid
Post Doctoral Fellow- Drug Discovery AIMachine Learning Hybrid
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Job Description
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.
- 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
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Employee Status:
RegularRelocation:
DomesticVISA Sponsorship:
YesTravel Requirements:
10%Flexible Work Arrangements:
HybridShift:
Not IndicatedValid Driving License:
NoHazardous Material(s):
N/ARequired Skills:
Algorithms Artificial Intelligence (AI) Computational Biology Computational Chemistry Computer Science Electrical Engineering Machine Learning Physics Statistical Inferences StatisticsPreferred Skills:
Job Posting End Date:
10/7/2025*A job posting is effective until 11:59:59PM on the day BEFOREthe listed job posting end date. Please ensure you apply to a job posting no later than the day BEFORE the job posting end date.
Employment Type
Full-Time
