Deep Learning Research Scientist – Structural Biology M/F

Adoc Talent Management is recruiting a PhD-level expert in deep learning methods applied to structural biology for its client.
Our client is a startup revolutionizing in silico approaches in drug discovery/repurposing and drug target identification by creating a digital twin of the cell, using a combination of breakthrough technologies at the crossroads of artificial intelligence, systems engineering, and single-cell multi-omics data analysis. This consists of identifying, a molecular target, such as a protein, that will trigger the transition from disease to healthy cells. With current methods, only 1 target in 10,000 reach the market, leading to a significant loss of time and efforts in the community.

The mission of the company is to revolutionize drug discovery and therapeutic innovation, providing disruptive methods to measure precisely cells activity at large scale, and build foundation models to mimic cell behavior in various contexts and identify the optimal trigger to reverse disease state. Half of the team today is dedicated to build the largest omics database, aka omics atlas to map all human body tissues and diseases and reduce experimental biases.

Collaborating with leaders in the pharmaceutical sector and top academic laboratories in the field, and following a successful funding round, the company is expanding its team.

Position

Your key responsibilities will include:

• Innovative Model Development: Design, implement, and optimize deep learning models that accurately predict protein protein, protein molecule, and RNA interactions using the latest advancements in deep learning and structural biology.
• Data Integration & Curation: Integrate experimental structural data with computational predictions to enhance model precision and reliability.
• Cross-functional Collaboration: Partner with structural biologists, bioinformaticians, and experimental teams to validate models and translate computational insights into actionable research outcomes.
• Model Benchmarking & Validation: Rigorously assess model performance using state-of-the-art benchmarking techniques and iterate based on new scientific insights.
• Project Leadership: Lead initiatives in computational structural biology, guiding projects from conception to execution while ensuring timely delivery and scientific excellence.

Profile

We are looking for a seasoned scientist with:

• Ph.D. in Computational Biology, Structural Biology, Bioinformatics, or a closely related field with a strong emphasis on deep learning and structural modeling and deep expertise in applying state-of-the-art deep learning approaches to structural biology challenges.
• Demonstrated experience designing and implementing state-of-the-art deep learning models for protein protein, protein molecule, and RNA interactions.
• A strong track record in integrating experimental structural biology data with computational modeling techniques to drive innovative solutions.
• Practical industry experience in a dynamic research or biotech setting.
• Proficiency in programming languages such as Python and familiarity with deep learning frameworks.
• Excellent communication skills, capable of conveying complex scientific concepts to interdisciplinary teams.
• Experience in a fast-paced, innovative R&D environment, with a record of high-impact research publications a plus.
• Fluent in English; French is a plus but not required.

Application

Our client offers a research friendly environment, with 90% of the company holding a PhD, with academic collaborations and publications. The team is international and composed of +10 different nationalities. The company is remote first as most of the work is remote and regular events organized in the offices in Paris.

If you want to become part of a forward-thinking team where your innovative contributions will directly influence cutting-edge discoveries in biomolecular interactions and help shape the future of drug discovery and therapeutic development, making a tangible impact on patient outcomes and scientific progress, please send your application (CV, list of publications, GitHub repository, cover letter, and salary expectations) to Adoc Talent Management without delay!