Postdoctoral Scholar Computational Chemistry

Johnson & Johnson Johnson & Johnson · Pharma · Beerse, Antwerp, Belgium

This role focuses on developing and employing quantum chemical simulations and machine learning workflows to advance the development of small molecule medicines and therapies. The primary focus is on estimating synthetic outcomes and assessing chemical degradation pathways in silico, with a secondary focus on developing and validating ML components for reaction outcome estimations.

What you'd actually do

  1. Develop and employ fast (hybrid) quantum chemistry workflows to accelerate reactivity assessments relevant to synthesis and drug substance stability, while maintaining fit‑for‑purpose accuracy for prioritization and decision making.
  2. Conduct computational studies to investigate chemical reactivity relevant to synthesis and drug substance stability, using quantum chemical and mechanistic modeling approaches.
  3. Develop, optimize, and validate machine learning components to improve reaction outcome estimations against limited experimental datasets and quantify model performance.
  4. Liaise with experimental scientists to define validation strategies, interpret results, and translate model outputs into actionable recommendations.
  5. Take part in research collaborations with academic and private organizations and consortia and communicate results through presentations and publications where appropriate.

Skills

Required

  • Ph.D. in Computational Chemistry, Chemistry, Data Science
  • experience in simulation technologies and/or AI/ML applied to chemistry
  • quantum chemical methods and/or reaction mechanism analysis
  • machine learning frameworks (e.g., PyTorch, TensorFlow)
  • scientific data analysis/model validation
  • work with heterogeneous experimental/computed datasets
  • statistical analysis
  • data visualization tools
  • Linux command‑line environment
  • Strong communication and collaboration skills

Nice to have

  • Exposure to condensed-phase reactivity problems
  • Insights into the broader pharmaceutical product development picture
  • workflow automation/scripting

What the JD emphasized

  • Ph.D. in Computational Chemistry, Chemistry, Data Science, with experience in simulation technologies and/or AI/ML applied to chemistry.

Other signals

  • developing and employing quantum chemical simulations and machine learning workflows
  • Develop, optimize, and validate machine learning components to improve reaction outcome estimations against limited experimental datasets