Senior Drug Discovery Scientist
DeepLife
- Paris
- CDI
- Temps-plein
- Method Development for Target Identification and Drug Repurposing: Develop SOTA large scale causal models on omics data, to learn cell response to perturbations and predict the optimal target and drugs to address a given disease.
- Lead an AI Team: Lead a multidisciplinary AI team by mentoring scientists and engineers, setting clear project goals, and fostering a culture of innovation and collaboration across computational and experimental disciplines.
- Interdisciplinary Collaboration: Work closely with biologists, engineer and stake holders to translate computational findings into actionable drug discovery insights.
- Continuous Learning and Innovation: Stay abreast of the latest advancements in computational biology, deep learning, and systems biology, continuously refining your methods and incorporating new technologies.
- Effective Communication: Present complex data and concepts clearly to both scientific and non-scientific audiences, including key stakeholders in pharmaceutical companies.
- Ph.D. in Computational Biology, Bioinformatics, Systems Biology, or a related field.
- Expertise in Deep Learning for Target Identification or Drug Repurposing: Proven track record in designing and deploying deep learning models (including generative, Bayesian, and causal models) applied to omics data to uncover novel drug targets.
- Network based Drug Repurposing Expertise: Demonstrated ability to apply network-based approaches to target identification and drug repurposing, utilising network medicine techniques to map and analyse complex biological interactions.
- Systems Biology Proficiency: Expertise in integrating multi-omics data and modelling biological systems to derive actionable insights.
- Single-Cell Analysis Experience: Extensive experience in computational biology with a focus on single-cell analysis to capture cellular heterogeneity.
- Interdisciplinary Team Player: Proven ability to work effectively within cross-functional teams and communicate complex concepts to diverse audiences.