MSc Student – FNP First Team

Descrizione

MSc Student – FNP First Team Sintesi in italiano (traduzione automatica): L'organizzazione Sano cerca uno studente di ricerca per un progetto innovativo sul microbioma umano, con sede a Sano. Il candidato lavorerà su un modello generativo per lo sviluppo di gemelli digitali e applicazioni biomediche, contribuendo a identificare biomarcatori di malattia e simulare dinamiche del microbioma. È richiesta l'iscrizione a un programma di laurea triennale o magistrale e lo stato di studente attivo. Il lavoro prevede 20 ore settimanali e una retribuzione di 2500 PLN mensili. Il candidato sarà supervisionato dal responsabile della ricerca e riceverà supporto tecnico e guida nella progettazione di modelli AI. Il progetto durerà fino al 2029 e culminerà in una tesi di laurea magistrale difesa con un rapporto di valutazione su diverse architetture. Project title : MSc Student - A generative foundation model of the human gut microbiome for digital twin development and biomedical applications – FNP First Team Publication date : 08.07.2026 Closing date : 07.08.2026 Level of education : BSc/Eng Hours : 20 hours per week (50% FTE) Salary indication : 2500 PLN gross monthly Supervisor : Head Researcher: Dr. Tomasz Kościółek (Structural and Functional Genomics Research Team Leader at Sano) Project period : 01.07.2026 – 31.12.2029 The human microbiome plays a fundamental role in many physiological processes including metabolism, immune regulation, and neurological signaling. Recent advances in metagenomic sequencing have enabled large-scale characterization of microbial communities across populations. However, translating microbiome sequencing data into functional insights and clinically actionable predictions remains a major challenge. The project, titled “A generative foundation model of the human gut microbiome for digital twin development and biomedical applications,” aims to create a next-generation model capable of supporting biomedical research, diagnostics, prevention, and therapy optimization. By training microbiomeGPT on large-scale metagenomic datasets, the team will work toward identifying disease biomarkers, simulating microbiome dynamics, and generating patient digital twins to advance precision medicine. This generative approach has the potential to transform how clinicians and researchers understand and intervene in human health. Support structure: The successful candidate will work under the Head Researcher conceptual supervision, with technical support from the Scientific Programmer (environment setup, training templates, reproducibility), and substantive guidance from prof. Ewa Szczurek (Helmholtz Munich) on AI model design and interpretation in biology. The candidate must be enrolled in a Bachelor's or Master's degree programme and hold active student status. Milestone: A defended master's thesis plus an evaluation report comparing at least 3 architectures (ideally 5 experiments), accepted by one of Sano's partner universities. The chosen architecture then feeds directly into Stage 4 (full training of microbiomeGPT v1.0). Annuncio in inglese. Fonte: Euraxess (Commissione europea).