Research Scientist in Protein Modeling and ML/AI 

Research Scientist in Protein Modeling and ML/AI

We are looking for a highly skilled and motivated Research Scientist (all genders) to join our team in Lyon (France) focused on protein-ligand modeling and ML (Machine Learning) approaches for protein structure prediction. Our candidate will possess a strong background in drug-discovery and additional knowledge of AI approaches for protein modeling research.

The successful applicant (all genders) will be part of an interdisciplinary and cross-divisional team within the R&D organization and will contribute to innovative industrial applications as well as pharmaceutical and crop science pipeline projects.

Main responsibilities

• Contribute to drug-discovery activities in pharmaceutical and crop science research by applying and developing state-of-the-art ML/AI and molecular modelling approaches.
• Challenge the status quo by formulating and testing hypotheses via in-silico models.
• Be an agile member of multiple interdisciplinary project teams (biology, chemistry, biochemistry, cloud computing), and efficiently communicate (written and orally) to different stakeholders at various levels of the organization.
• Clearly communicate project progress, impact to relevant stakeholders, and ensure delivery on time.

Your profile

• PhD in computational chemistry, computational biophysics, cheminformatics, or related field.
• Solid understanding of molecular modelling techniques applied to drug design (e.g., structural bioinformatics, docking and virtual ligand screening, molecular dynamics, and related techniques) is required. Basic knowledge of biology and biochemistry is highly appreciated.
• Proficiency in Python and demonstrated experience with machine learning algorithms and tools (e.g., Catboost, RDkit) & deep learning frameworks (e.g., Keras, TensorFlow, PyTorch) and good practices in software development (e.g., git, module design).
• Knowledge of data visualization, experience running calculations via cloud computing and HPC (High Performance Computing) platforms is desirable.
• Proven expertise in applying machine learning and artificial intelligence techniques to computational chemistry and protein structure prediction.
• Strong problem-solving skills, ability to structure and simplify complex tasks.
• Ability to work in interdisciplinary teams with excellent interpersonal and collaborative skills.
• Solid written and oral communication in English.

We offer

• Fixed-term contract for 2 years.
• Competitive salary.
• Good working conditions and comfortable working environment.
• We support flexible working time models and the option of partially working from home.
• Location: Lyon (France)

Qui sommes-nous  ?

Bayer est un groupe international dont les cœurs de métier se situent dans les secteurs de la santé et de l'agriculture. A la confluence de la chimie et de la biologie, Bayer contribue à améliorer la qualité de vie en proposant des produits et services en santé humaine et végétale.  

Chez Bayer nous sommes chaque jour motivés par les grands défis de notre époque. Notre ambition est de contribuer à un monde où la santé pour tous, la faim pour personne ne soient plus un rêve mais une possibilité, en protégeant chacun au quotidien. Dynamisme, curiosité et engagement guident nos actions. Nous pensons que nous pouvons apprendre de tous ceux qui nous entourent, en élargissant nos points de vue, en développant nos compétences, en faisant évoluer le statu quo. Il y a tellement de raisons de nous rejoindre. Vous avez envie d’une carrière riche de sens et qui offre des opportunités variées ? Vous souhaitez rejoindre des équipes expertes et diverses qui permettent le changement ? Le choix est simple ! 
 
Pour en savoir plus :  

// Découvrez notre site internet en France : Bayer France  
// Découvrez comment nous transformons l’agriculture : Transformer l’agriculture  

 
"Pour Bayer, l’égalité des chances est un axe majeur de la responsabilité sociétale de l’entreprise "
 

Lieu:

France : Rhône : Lyon  

Division:

Crop Science 

Code de référence:

823128