Postdoctoral Research Fellow (Bioinformatics and AI-Assisted Evidence Synthesis) / Tutkijatohtori (Bioinformatiikka ja tekoälyavusteinen evidenssisynteesi)

Tiivistelmä työpaikkailmoituksesta: The Postdoctoral Research Fellow will lead computational work in bioinformatics, AI-assisted evidence synthesis and computational mechanistic toxicology within the EIRIS project on endocrine-disruption-induced endometriosis. The role involves developing transparent AI/NLP workflows and biocuration, analysing high-dimensional omics data, applying pathway/network and mechanistic modelling, and building reproducible computational pipelines. Applicants must have a relevant doctoral degree, strong Python and/or R skills, substantial experience with omics analysis and NLP/LLMs, independent working ability, and excellent English (C1). The position is full-time for 2 years with a six-month trial, based at Tampere University, with a typical starting salary of approximately 3800–4000 EUR per month plus performance-based pay and an application deadline of 10 September 2026. Tämä tiivistelmä on luotu tekoälyn avulla. Tampere University and Tampere University of Applied Sciences create a unique environment for multidisciplinary, inspirational and high-impact research and education. Our university community has its competitive edges in technology, health and society. www.tuni.fi/en . We are looking for a Postdoctoral Research Fellow in the field of bioinformatics, AI-assisted evidence synthesis and computational mechanistic toxicology. Job description We are seeking a computationally strong and independent Postdoctoral Research Fellow to shape computational work in the MAT group. The position will be primarily connected to the project EIRIS: Endocrine-disruption-Induced Endometriosis and Reconstruction of Integrated Signaling , while also contributing to broader group activities working at the interface of biomedical data science, systems biology, computational toxicology, and regulatory science (applied toxicology). EIRIS is a 4-year project that investigates how environmental chemicals may disrupt endocrine mechanisms leading to endometriosis, a common and under-researched condition affecting approximately 10–15% of women of reproductive age. The project aims to integrate fragmented biomedical evidence into structured resources and predictive models that support non-animal approaches to chemical safety assessment and women’s health research. The position is hosted by the Mechanistic and Applied Toxicology (MAT) group, a newly established research group led by Dr Alexandra Schaffert at the Faculty of Medicine and Health Technology, Tampere University. MAT develops mechanistic approaches for chemical safety assessment, contributing to the shift from animal-based toxicology toward Next-Generation Risk Assessment and non-animal methods (NAMs). We combine in vitro molecular biology, mass-spectrometry-based proteomics, bioinformatics, evidence synthesis and computational modelling to understand how environmental chemicals perturb biological systems, with a particular focus on computational toxicology, endocrine disruption and female health. A central goal of the group is that mechanistic discoveries do not remain purely academic, but translate into tools and evidence that help decide which chemicals are safe, which biological effects matter, and how non-animal testing strategies should be designed in regulations. The group is embedded in the multidisciplinary environment of Tampere University, with access to strong local expertise and infrastructure in toxicology, high-performance computing, mass spectrometry and non-animal method development. As part of our team, you will have the opportunity to: AI-assisted evidence synthesis and knowledge structuring Develop workflows for extracting, structuring and evaluating information from biomedical text using natural language processing, language model-based or other AI-assisted approaches in a transparent, reproducible and critically evaluated manner Contribute to biocuration, semantic interoperability, ontology-aware data integration and the development of structured evidence resources Bioinformatics and mechanistic data integration Analyse and integrate high-dimensional biomedical datasets, including transcriptomics, proteomics, secretomics and other molecular data Apply pathway, network, graph-based and mechanistic modelling approaches to unravel adverse effects of environmental chemical exposure Contribute to Adverse Outcome Pathway (AOP) research, including quantitative AOP approaches (AOPs are evidence-weighted causal graphs linking a molecular initiating event over several key events to an adverse outcome across multiple biological levels) Build documented, reproducible and reusable computational workflows for collaborative research Scientific contribution and mentoring Contribute to scientific articles, presentations and grant applications Co-supervise junior researchers Prior knowledge of toxicology or biomedical research is beneficial but not required. We are looking primarily for a strong computational researcher with curiosity and mot