Postdoctoral Researcher – Machine Learning

Descrizione

Postdoctoral Researcher – Machine Learning Sintesi in italiano (traduzione automatica): L'organizzazione internazionale cerca un Ricercatore post-dottorato specializzato in Machine Learning per lavorare nel campo della scoperta di farmaci antivirali. La sede è un laboratorio di screening automatizzato ad alta biosicurezza. Il candidato sarà responsabile dello sviluppo e dell'implementazione di modelli avanzati di machine learning per analizzare dati complessi di imaging e selezionare molecole per studi virologici approfonditi. È richiesta una laurea di dottorato in machine learning, informatica, bioinformatica o equivalente, insieme a competenze nella creazione e valutazione di modelli di machine learning e familiarità con framework di deep learning come PyTorch o Tensorflow. È preferibile avere esperienza con dati di imaging cellulare e conoscenze in chemo-informatica. Il candidato ideale deve possedere forti capacità analitiche, essere proattivo e in grado di lavorare in team. Antiviral drugs are used to successfully treat infections such as with HIV and HCV. Yet, for most (life)-threatening and neglected infections, there are no such drugs. This leaves also critical gaps in epi- and pandemic preparedness. Antiviral drug discovery efforts typically focus on a few known targets. Yet, the biology of viral replication consists of many more complex processes that should harbor a wealth of undiscovered druggable targets. Thus, a large space of potential druggable biology is entirely ignored. We aim to fundamentally revolutionize antiviral target-discovery by uncovering this terra incognita. To that end, we developed high-throughput, multiplex, high-content multiparametric phenotypic antiviral assays. These allow to screen hundreds of thousands of molecules in our fully automated high biosafety screening facility CAPS-IT against multiple viruses. You will be responsible for the development and deployment of advanced machine learning models that leverage the full complexity of the imaging data and that allow the selection of molecules that will serve for in-depth virological studies. Ultimately, this will result in the establishment of the first-of-its-kind “Atlas of Druggable Antiviral Targets”.You will join a dynamic, multidisciplinary and international virology-team with state-of-the-art infrastructure, but will at the same time also be embedded in a team with extensive expertise in AI and machine learning for computational biology and chemo-informatics. This will provide the opportunity to design novel machine learning approaches that leverage state-of-the-art AI methods (deep learning, generative AI, Bayesian modelling, active learning, etc.) to combine cellular imaging data, chemical compound structure, viral genomes and other omics data.You will take ownership of the implementation and optimization of ML-driven models in our antiviral screening pipeline thereby unlocking the full richness of the multi‑parametric data using advanced AI. You will extract and interpret fully detailed phenotypic fingerprints at whole-well and single‑cell resolution in virus-infected cell cultures. AI models will be used to cluster compounds and infer possible mechanisms of action, identify peculiar activity signatures and integrate cellular toxicity profiles to reduce false positives and guide compound prioritization. The models will be populated and iteratively refined by converging evidence from downstream validation (such as chemo-genetics, structural modelling, functional assays, thermal proteome profiling and omni-omics), creating an adaptive and constantly evolving discovery pipeline. AI-driven interpretation will exploit the full complexity of the dataset to expand the druggable antiviral target space. We seek a researcher with strong machine learning modelling expertise with experience in the analysis of challenging large-scale data sets. Experience with cellular imaging data or virology/immunology are a plus.Relevant skills include:• Experience in creating and evaluating machine learning models.• Familiarity with deep learning framework, such as PyTorch or Tensorflow.• Experience in data preparation, preferably in a bioinformatics context (data cleaning, filtering, etc.).• Expertise in data fusion and relevant algorithms (deep learning, generative AI, kernel methods, Bayesian methods). • Preferably, experience with high-content imaging or cell imaging data (e.g., CellProfiler, convolutional neural networks).• Knowledge of chemo-informatics and drug discovery.• Strong practical statistical skills (batch effects, confounders, experiment design). You hold a PhD in machine learning, computer science, bioinformatics or equivalent. You combine strong analytical skills with the ability to work independently and lead collaborative efforts. You are a team player, proactive, solution-oriented, and comfortable taking ownership of complex projects. Excellent English communication skills and a strong publication record are Annun