Research Scientist/PostDoc Modelling for Bioavailability Predictions
Bayer
Leverkusen, Germany
vor 2 Tg.

and responsibilities

  • Develop predictive models for humanbioavailability
  • Develop and integrate chemical-structure-based deeplearning models with physiology-based pharmaco- / toxicokinetic(PBPK / PBTK) models
  • Design, compose and deliver presentations andpublications
  • Collaborate and interact with an international,interdisciplinary and cross-divisional team comprising experts frompharmaco-
  • and toxicokinetics, computational chemistry and deeplearning

    Who you are

  • PhD in data science, mathematics, (computational)biology / chemistry, pharma- / toxicology or a related field withexperience in machine learning and mechanisticalmodelling
  • Profound knowledge of R and / or Python
  • Familiarity and good understanding of state-of-the-artmachine learning methods, model selection and deep learningconcepts and strong willingness to further develop expertise inthese areas
  • Experience with mechanistic modelling is aplus
  • Combining strong theoretical and analytical skills withprogramming experience
  • Excellent written and verbal communication skills inEnglish in an interdisciplinary environment
  • The position is limited for 2 years and fundedthrough a Bayer Life Science Collaboration grant program, whichpromotes state-

    of-the-art research within the global Bayerorganization with a special focus on cross-divisionalexchange.

    Your application

    Are you looking for a new challenge where you can show your passion for innovation? Are you interested in working as part of a global team to improve people’s lives?

    Then send us your online application including cover letter, CV and references.

    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.

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