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At Bayer we’re visionaries, driven to solve the world’s toughest challenges and striving for a world where ,Health for all, Hunger for none’ is no longer a dream, but a real possibility. We’re doing it with energy, curiosity and sheer dedication, always learning from unique perspectives of those around us, expanding our thinking, growing our capabilities and redefining ‘impossible’. There are so many reasons to join us. If you’re hungry to build a varied and meaningful career in a community of brilliant and diverse minds to make a real difference, there’s only one choice.

 

Senior Scientist (all genders) - Computational Design for Biologics 

 

 

You will contribute to innovative biologics discovery projects by designing novel antibodies and biologics-based therapeutics. In this role, you will work with interdisciplinary teams, including structural biologists, computational chemists, and discovery scientists across disciplines, to accelerate the design and optimization of therapeutic candidates. If you combine scientific excellence with a strong sense of ownership in self-organized teams, we look forward to receiving your application.

 

YOUR TASKS AND RESPONSIBILITIES

 

  • You initiate, drive, and support drug discovery projects as an active member of highly interdisciplinary, cross-functional teams
  • You apply structural and biological insights to connect targets with appropriate therapeutic modalities
  • Defining tailored lead-finding strategies, including the characterization of protein-protein and protein-ligand interactions, identification of druggable pockets/epitopes, and design of biomolecules, is part of your area of responsibility
  • You design and optimize antibodies using structure- and sequence-based computational as well as AI-driven methods in close collaboration with antibody engineering and machine learning partners
  • You build and apply modeling pipelines for affinity, specificity, stability, developability, and manufacturability assessments, including aggregation/solubility risk and PTM liability evaluation
  • The generation and prioritization of design hypotheses through the integration of structural, sequence/omics, biophysical, assay, and literature data is a key part of your role
  • You develop automated workflows for candidate generation, scoring, selection, and data integration across programs
  • As part of your role, you communicate results to diverse audiences through concise reports, visualizations, and presentations, while working closely with wet-lab and IT/HPC stakeholders

 

WHO YOU ARE

 

  • You hold a PhD in Computational Biology, Biophysics, Bioinformatics, (Bio-)Chemical Engineering, Structural Biology, or a related discipline
  • You bring knowledge of biologics discovery and can translate scientific literature into industrial practice
  • You have deep expertise in protein/peptide structure modeling, antibody design, molecular dynamics, free-energy and physics-based scoring, as well as ML-enabled design approaches, including modern AI tools for co-folding, structure prediction, and de novo binder generation
  • You have experience working with and interpreting large experimental datasets, including binding and functional assays, biophysical characterization, and developability screens
  • You demonstrate strong problem-solving skills and work effectively both independently and collaboratively in cross-functional environments
  • You enjoy working in self-organized teams and take ownership and accountability across the entire value chain
  • Experience with large-scale computing environments (HPC/Cloud), container technologies, and workflow orchestration is considered a plus
  • Excellent communication and negotiation skills, strong intercultural competence, a collaborative mindset, and fluency in English, both written and spoken, complete your profile

 

ADDITIONAL INFORMATION

 

This is a fixed-term position for 24 months. 

 

WHAT WE OFFER

 

Our benefits package is flexible, appreciative, and tailored to your lifestyle, because: What matters to you, matters to us! 

 

  • We ensure your financial stability with a competitive salary between  77.300€ and  93.800€ per year (full-time) plus a variable component. Your compensation is based on your qualifications, skills, and professional experience.  
  • Whether it’s hybrid work models or part-time arrangements: Whenever it is possible, you will have the flexibility to work how, when and where it is best for you.
  • Your family is a top priority. We offer loving company daycare centers at multiple locations, support in finding childcare, time off for the care of elderly or dependent family members, summer camps for children, and much more.
  • We support your professional growth by providing access to learning and development opportunities, training programs through the Bayer Learning Academy, development dialogues, as well as coaching and mentoring programs.
  • We promote health awareness and opportunities for selfcare through various measures, such as free health checks with the company doctor.
  • We embrace diversity by providing an inclusive work environment in which you are welcomed, supported, and encouraged to bring your whole self to work.

 

Be You. Be Bayer.

 

#LI-AMSEMEA

 

YOUR APPLICATION

 

This is your opportunity to tackle the world’s biggest challenges with us: Maintaining our health, feeding growing populations and slowing the rate of climate change. You have a voice, ideas and perspectives and we want to hear them. Because our success begins with you. Be part of something big. Be Bayer.

 

Bayer welcomes applications from all individuals, regardless of race, national origin, gender, age, physical characteristics, social origin, disability, union membership, religion, family status, pregnancy, sexual orientation, gender identity, gender expression or any unlawful criterion under applicable law. We are committed to treating all applicants fairly and avoiding discrimination.
 

 

Location:            ​   Wuppertal-Aprath

Division: ​               Pharmaceuticals

Reference Code:  877334

 


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