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Material and Analytical Sciences - Co-Op

Compensation Data

This position offers an hourly rate of $20.00 - $33.00 USD / hr. commensurate to the level of degree program in which an applicant is actively enrolled. For an overview of our benefits please click here.   

Description

Boehringer Ingelheim is currently seeking a talented and innovative Co-Op to join our Molecular Structure team in our Material and Analytical Sciencies department located at our Ridgefield, CT facility. As a Co-Op, you will be playing a critical role in revolutionizing pharmaceutical solid-state structure determination through an innovative integration of computational and experimental approaches. You will develop a rapid workflow combining solid-state ¹⁵N NMR chemical shift data with DFT First Principles calculations and machine learning algorithms to refine three-dimensional atomic structures of pharmaceutical compounds. In this role, you will develop cutting-edge computational tools for NMR crystallography focused on pharmaceutical structure elucidation. This position offers hands-on experience at the intersection of machine learning, quantum chemistry, and pharmaceutical development.

As an employee of Boehringer Ingelheim, you will actively contribute to the discovery, development and delivery of our products to our patients and customers. Our global presence provides opportunity for all employees to collaborate internationally, offering visibility and opportunity to directly contribute to the companies´ success. We realize that our strength and competitive advantage lie with our people. We support our employees in a number of ways to foster a healthy working environment, meaningful work, mobility, networking and work-life balance. Our competitive compensation and benefit programs reflect Boehringer Ingelheim´s high regard for ouremployees.

Duties & Responsibilities

  • Predict ¹⁵N NMR chemical shifts for various pharmaceutical polymorphs using density functional theory (DFT) First Principles calculations and Machine Learning
  • Use Machine Learning algorithms (e.g. GNN, MLOP) to compliment DFT First Principles Calculations for structure predictions with increased accuracy.
  • Benchmark and optimize computational protocols for accuracy and efficiency
  • Compare computational predictions against experimental ¹⁵N NMR datasets (already available)
  • Develop and implement Bayesian statistical models to integrate predicted and experimental chemical shift data
  • Add to the existing OPTICS approach for improved polymorph differentiation (see publication: https://doi.org/10.1021/acs.cgd.1c00797)
  • Train machine learning algorithms to accelerate structure refinement workflows
  • Characterize hydrogen bonding networks in pharmaceutical solids
  • Validate polymorph identification capabilities across multiple case studies
  • Create an integrated computational pipeline combining DFT calculations, machine learning predictions, and experimental NMR constraints
  • Optimize workflow for computational efficiency and scalability to multiple pharmaceutical compounds
  • Document methodologies and develop user-friendly tools for broader team adoption

Requirements

  • Must be a current  graduate or advanced degree student in good academic standing.
  • Student must be enrolled at an accredited college or university for the duration of the Co-Op.
  • Overall cumulative minimum GPA from last completed quarter/semester 3.0 GPA (on a 4.0 scale) preferred.
  • Major or minor in related field of Co-Op.
  • Graduate and advanced degree students must have completed at least 9 credit hours at current college or university.
  • Strong computational chemistry background with hands-on DFT experience (CASTEP, Gaussian, ORCA, Quantum Espresso, XTB or similar)
  • Proficiency in machine learning frameworks and statistical analysis (scikit-learn, TensorFlow, PyTorch, MLOP, GGN, SOAP, SHIFTML2 or equivalent)
  • Solid understanding of crystal structure prediction
  • Excellent programming skills, particularly in Python for scientific computing
  • Experience with structural analysis and molecular modeling
  • Solid understanding of quantum chemistry principles and DFT methodologies, including plane wave and periodic DFT calculations.
  • Knowledge of structure refinement concepts
  • Understanding of Bayesian statistics and clustering algorithms


Eligibility Requirements:

  • Must be legally authorized to work in the United States without restriction.
  • Must be willing to take a drug test and post-offer physical (if required).
  • Must be 18 years of age or older.

Desired Skills, Experience and Abilities

•    Experience with solid-state NMR or pharmaceutical crystallography
•    Familiarity with high-performance computing (HPC) environments, linux shell scripting
•    Experience with all-atom molecular dynamics simulations and QM calculations
•    Integrating machine learning with structure prediction
•    Proficiency with scientific data visualization and analysis tools

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