Postdoctoral Scientist, Deep Learning / Machine Learning - Drug Discovery

$77,000 - $106,000/Yr

Johnson & Johnson - Juneau, AK

Full-time - Entry Level

Remote - Juneau, AK

5,001-10,000 employees

Chemical Manufacturing

About the position

Johnson & Johnson Innovative Medicine is currently seeking a Postdoctoral Scientist, Deep Learning / Machine Learning to join our In Silico Drug Discovery team. The primary and preferred location is Cambridge, MA, although remote work options in the US may be considered on a case-by-case basis. This position is a 2-year role that focuses on integrating quantum mechanics (QM) data with deep learning (DL) models to advance molecular predictive modeling. The successful candidate will work closely with experts across various domains to develop innovative methods and tools that will enhance our drug discovery processes. At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity. The ideal candidate will have a strong background in deep learning and machine learning, with a keen interest in applying these skills to drug discovery. We are looking for a candidate with publications in high-level venues like NeurIPS, ICML, or ICLR, a background in drug discovery, demonstrated software skills, and knowledge of Python, PyTorch, and other DL-related tools. The candidate will be expected to collaborate effectively with cross-functional teams, contribute to the development of state-of-the-art methods, and ensure the reproducibility of experimental results. This is an exciting opportunity to be at the forefront of pharmaceutical innovation, leveraging advanced computational techniques to make a significant impact on healthcare.

Responsibilities

• Research and develop deep learning methods to effectively train graph neural networks for Quantum Mechanics and other physics informed data.
• Contribute to the creation of comprehensive reports and scientific publications documenting the methods and results of research projects. Publish findings in high-level conferences and journals.
• Participate in team meetings, brainstorming sessions, and collaborative projects to drive innovation and solve complex problems.
• Work closely with cross-functional teams, including data scientists, chemists, and biologists, to integrate deep learning models into the drug discovery pipeline.

Requirements

• PhD in machine learning, computer science, applied mathematics or relevant field (completed within the last 2 years, or expected to be completed in 2024) is required.
• Demonstrable expertise in developing deep neural networks (transformers, Bayesian neural nets, RNN, CNN) is required.
• Demonstrable expertise with Deep Learning frameworks, like PyTorch, Keras, Tensorflow is required.
• Demonstrable expertise in work with large datasets is required.
• Expertise in GPU computing is required.
• Ability to present and communicate with stakeholders is required.
• Ability to translate data into information and strategies into executable action plans is required.

Nice-to-haves

• Publications at top-tier Machine Learning conferences (i.e., NeurIPS, ICML, ICLR) are strongly preferred.
• Experience developing Deep Learning methods to train graph neural networks is preferred.
• Knowledge in Cheminformatics is desirable.

Benefits

• Medical, dental, vision, life insurance, short- and long-term disability, business accident insurance, and group legal insurance.
• Consolidated retirement plan (pension) and savings plan (401(k)).
• Vacation - up to 120 hours per calendar year.
• Sick time - up to 40 hours per calendar year; for employees who reside in the State of Washington - up to 56 hours per calendar year.
• Holiday pay, including Floating Holidays - up to 13 days per calendar year.
• Work, Personal and Family Time - up to 40 hours per calendar year.