Associate Scientist, Post Doctoral Fellow- Computational Reaction Modeling - Machine Learning

Merck & Co.•Rahway, NJ

60d•$75,000 - $86,000•Hybrid

About The Position

Postdoctoral Research Fellow - Modeling & Informatics 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. Position Overview: We are seeking a Postdoctoral Research Fellow with expertise in computational molecular modeling to join the Modeling and Informatics (M&I) group in Rahway, New Jersey, USA. The successful candidate will work closely with computational and experimental experts to develop novel, robust, and practical computational workflows for predicting reactivity, selectivity, and catalyst performance in organic reactions of importance in the pharmaceutical industry. Leveraging both classical techniques in physical organic chemistry, including reaction modeling, and cutting-edge machine learning-based methods, the Postdoctoral Fellow will collaborate with colleagues across Discovery and Process Chemistry to elucidate, influence, and design experiments related to reaction discovery and catalyst design.

Requirements

Ph.D. completed within 6 months of hire in Computational Chemistry / Machine Learning / Chemical Engineering, or a closely related field
Strong academic background in machine learning, computer science, cheminformatics, physical chemistry, organic chemistry, or applied mathematics
Demonstrated experience applying machine learning to chemical problems
Strong programming skills with Python, including PyTorch and TensorFlow
Experience with classical organic reaction modeling including, but not limited to, quantum chemical calculations and conformational searches
Experience with developing machine learning-based atomistic potentials
Ability to independently drive and deliver results, while balancing multiple priorities simultaneously
Proven ability to communicate technical findings clearly in writing and presentations to a cross-functional audience including synthetic chemists, data scientists, and IT teams

Nice To Haves

Expertise in reaction modeling and ligand design for catalysts
Experience with cheminformatics techniques, such as molecular property predictions, molecular featurization ( e.g., RDKit)
Experience applying machine learning to large chemical datasets for predictive purposes

Responsibilities

Conduct research focused on the benchmarking and fine-tuning of machine learning-based interatomic potentials, both independently and in collaboration with an interdisciplinary team of molecular modelers and experimental scientists across Discovery Chemistry and Process Chemistry
Couple molecular modeling and cheminformatics tools to develop novel workflows for reaction modeling at a scale relevant for drug discovery and process development
Independently plan, execute and clearly communicate key aspects of a research program
Attend and participate in meetings across departments, presenting project progress to various teams
Author scientific publications and present at conferences