Automation & Data Science Co-op — Materials & Analytics

Boehringer Ingelheim Pharmaceuticals, Inc.; Ridgefield (CT); Sur place; USD 33 000 - 45 000; Temps partiel

Avantages offerts par ce poste

Medical coverage

Paid time off

Paid holidays

Disability and life insurance

Résumé du poste

Boehringer Ingelheim is seeking an Intern to join the Material and Analytical Sciences department at our Ridgefield, CT facility. You will expand your scientific skills, use state-of-the-art equipment, and gain insights into the drug development process.

The role involves designing automated solubility experiments, analyzing results with data analytics and machine learning, and developing automated workflows for isolation process development.

Qualifications

• PhD candidate in chemical engineering, pharmaceutical engineering, chemistry, materials science, data science, or related field.
• Self-motivated and eager to learn, explore, and conduct independent research.
• Familiarity with thermodynamic models such as NRTL, UNIFAC, and UNIQUAC.
• Experience using Python or other programming tools for data analytics, machine learning, computer vision, and laboratory automation.

Responsabilités

• Design and conduct automated solubility experiments using robotics and high-throughput tools.
• Analyze experimental results using modeling, data analytics, machine learning, and computer vision.
• Develop a digital, automated workflow to support isolation process development.

Connaissances

Python programming

Data analytics

Machine learning

Computer vision

Laboratory automation

Robotics

Independent research

Formation

PhD candidate in chemical engineering

Outils

High-throughput tools

Description du poste

Boehringer Ingelheim is seeking an Intern to join the Material and Analytical Sciences department at our Ridgefield, CT facility. You will expand your scientific skills, use state-of-the-art equipment, and gain insights into the drug development process.

The role involves designing automated solubility experiments, analyzing results with data analytics and machine learning, and developing automated workflows for isolation process development.