The Position You aim to make a transformative impact by integrating machine-learning methods into drug discovery projects? We are seeking you, a passionate and talented postdoctoral researcher who shares our ambition to design innovative predictive modelling approaches to optimally leverage available data and thereby advance drug discovery. In particular, the reliable prediction of crucial drug candidate properties, like pharmacokinetic (PK) profiles, in sparse and scattered data regimes are in focus. This Position is limited for 2 years. Note: To make it easier to find our job advertisements, we use the usual designation "Post Doc". Of course, this advertisement is not only addressed to applicants directly after completing their doctorate, but to all qualified candidates. Tasks & responsibilities In your new role as a Post Doc you will be responsible for developing advanced machine learning methods including reliable uncertainty estimation, in collaboration with local experts in the field, to drive our ML-based PK-modelling initiative. By leveraging cutting-edge knowledge on meta-information integration and multitask approaches, you will select, prioritize, and further develop approaches to predict small molecule properties that impact the in vivo performance of drug substances and thus you will generate a tangible impact to drug discovery projects. Based on enhanced inhouse expertise in predictive modeling and machine learning and together with technology experts, you will develop approaches to integrate experiments and predictions as well as uncertainty estimates for data-driven decision making. Moreover, you will actively contribute to the realization and success of a collaboration with external partners, leveraging additional data sources to enhance predictive modeling capabilities. In an interdisciplinary team, you will set up a strategy that combines experimental results with predictions to enable optimized design of in vivo studies and prioritization of compounds for synthesis. You will drive the publication of research results in close collaboration with your team, ensuring high-quality scientific output and visibility. Furthermore, you will take an active role in Boehringer Ingelheimu2019s global post-doctoral community by presenting your work at international conferences and Post Doc Days, while gaining valuable insights into drug discovery strategies and exploring opportunities for your career development. Requirements PhD in Life Sciences, Computer Science or related disciplines Strong programming skills in Python are mandatory, experience with RDKit is considered an asset Hands-on experience with state-of-the-art machine learning techniques Strong analytical and problem-solving skills Excellent communication & interdisciplinary skills (publication record, etc.) Fluency in English (written and spoken) Ready to contact us? If you have any questions about the job posting or process - please contact our HR Direct Team, Tel: 49 (0) 6132 77-3330 or via mail: hr.de@boehringer-ingelheim.com Recruitment process: Step 1: Online application - The job posting is presumably online until January 4th, 2026. We reserve the right to take the posting offline beforehand. Applications up to December 22nd, 2025 are guaranteed to be considered. Step 2: Virtual meeting in the period from mid-December till end of January Step 3: On-site interviews beginning of February Please include the following documents when applying for this position: CV, Cover Letter Please submit your application documents in English. Discover our Biberach site: xplorebiberach.com All qualified applicants will receive consideration for employment without regard to a personu2019s actual or perceived race, including natural hairstyles, hair texture and protective hairstyles; color; creed; religion; national origin; age; ancestry; citizenship status, marital status; gender, gender identity or expression; sexual orientation, mental, physical or intellectual disability, veteran status; pregnancy, childbirth or related medical condition; genetic information (including the refusal to submit to genetic testing) or any other class or characteristic protected by applicable law.