OHAMR Call for proposals 2026

General Information

• Type: Partner looking for project
• Organisation: Universität Leipzig
• Country: Germany (DE)
• Career stage: Other.

Research area

• Scientific area(s) of the call:
  1. Topic 1: Identify and develop new combination treatments using existing or innovative antimicrobials or antimicrobial with adjunctive treatments to extend drug efficacy and combat resistance.
• One Health Setting:

  H - Human Health

  A - Animal Health

• Keywords:

  combination therapy; resistance prediction; multi-omics integration; Bayesian modelling; external validation

• Brief description of your expertise / expertise you are looking for:

  Expertise (GenStat, University of Leipzig): Statistical and machine-learning methods for high-dimensional biomedical data and multi-source integration. We contribute quantitative approaches for identifying and optimising antimicrobial/antifungal combination therapies, including dose–response and synergy modelling, Bayesian/ML frameworks, uncertainty quantification, and robust validation across datasets. We also support analysis plans, reproducible pipelines, and data harmonisation across sites. Looking for: AMR-focused partners (clinical microbiology, infectious diseases, mycology, pharmacology/PK-PD, veterinary or plant health) generating in vitro / ex vivo / in vivo or clinical datasets on candidate combinations (including adjunctive therapies), ideally with paired susceptibility/resistance measurements and metadata enabling cross-site validation.

• Brief description of your project / the project you would like to join:

  We are looking to join a transnational consortium under Topic 1 where GenStat can lead or co-lead the quantitative modelling and integration work package. Possible contributions include: (i) statistical design and analysis of combination screens; (ii) modelling synergy/antagonism and identifying robust candidate combinations; (iii) linking phenotypic response profiles with molecular data (e.g., genomics/transcriptomics/proteomics/metabolomics, where available) to understand mechanisms and resistance trajectories; and (iv) external validation and generalisation assessment across sites and One-Health settings. We are flexible regarding pathogen focus (bacterial or fungal) and can align to consortium datasets and experimental pipelines.

Peter Ahnert

• Organisation: Universität Leipzig
• Position: Senior Scientist, scientific project management
• E-mail: peter.ahnert@imise.uni-leipzig.de
• Phone: +49-341-9716282
• Web: www.genstat.imise.uni-leipzig.de/