Job Details

About the Company: A innovative pharmaceutical company seeking expertise in the technical and application areas described in this position. The role offers a focused setting for translating specialist knowledge into research, engineering or organizational outcomes.

Target Candidates: PhD in AI, Computer Science, Data Science, Bioinformatics, Pharmacy, Medicine, Computational Chemistry or Biostatistics.

Responsibilities

•  Apply AI to target discovery, molecule screening, drug discovery and translational research.

•  Develop pharmaceutical data-analysis, machine-learning, deep-learning, generative, knowledge-graph and multimodal methods.

•  Integrate compound, target, omics, literature, clinical and experimental data for design, activity/toxicity prediction and indication expansion.

•  Partner with chemistry, biology and clinical teams to deploy validated models in real R&D workflows.

•  The position connects algorithm design with data preparation, model evaluation and deployment in the application setting described below. Work should be documented so that technical results can be assessed against the needs of the relevant users and project team.

Requirements

•  PhD in AI, Computer Science, Data Science, Bioinformatics, Pharmacy, Medicine, Computational Chemistry or Biostatistics.

•  Machine learning, GNNs, generative models, NLP, knowledge graphs or multimodal modeling; Python and PyTorch/TensorFlow skills.

•  Ability to connect algorithms to drug-development problems; AIDD project and biomedical-data experience preferred.

•  Candidates should be able to explain modeling choices, evaluate results with appropriate evidence and communicate technical trade-offs to collaborators from different disciplines.

Highlights & Support: Cross-disciplinary opportunity to implement AIDD use cases within an innovative-drug R&D organization.