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in the jobs section of the IRBLleida website (https://www.irblleida.org/en/jobs/) before the 31st of January 2026. To formalize their application, candidates must submit the following: Candidate’s CV
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. Antonio Scialdone’s group at Helmholtz Munich, a leading European hub for AI in biology. The successful candidate will design and implement physics-informed machine learning frameworks and predictive models
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and inclusive community in the practice and teaching of science. Successful candidates will be expected to establish a vibrant research program, teach graduate and undergraduate courses, and participate
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hypotheses. Develop, refine, and benchmark computational pipelines using statistical modeling, machine learning, and deep learning approaches. Conduct analytical validation studies including precision
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computational pipelines for multiplex imaging, spatial transcriptomics, single cell RNAseq, and multi-omics data integration. Lead graph-based network and machine learning analyses of tumor immune
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for experimentation, yet they remain difficult to deploy directly onboard robots due to hardware availability, latency, sampling cost, and noise. Previous work on quantum machine learning (QML) emphasize
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, including approaches that produce “black box” data that might only be actionable in conjunction with AI and machine learning methods. Experimental technologies could cover (but are not limited to) single-cell
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to uncover new ideas and share their discoveries, health professionals to stay at the forefront of medical science, and educators to advance learning. We are proud to be part of progress, working together
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with experts to automate diagnostic assays, leading to cost-effective, easy to use tests Work closely with AMR, Informatic and Machine Learning colleagues ensure the tests provide accurate pathogen ID
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programme at the Faculty of Science . The ideal candidate has a background in or experience with one or more of the following topics: Advanced deep learning architectures Mathematical foundations of machine