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learning) and image processing, An interest in optical instrumentation and the medical field, Programming and machine learning skills, Ability to collaborate in an interdisciplinary team, Analytical and
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, proteomics, long-read sequencing). Familiarity with machine learning approaches, particularly artificial neural networks, and their application to biological data. Experience with workflow management systems
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to defining specifications and requirements for the FDI system • Development and evaluation of model-based and machine learning-based FDI algorithms, in close exchange with relevant stakeholders • Close
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candidate will have recently completed (or be close to completing) a PhD in Computer Science, Machine Learning, Natural Language Processing (NLP), or a related field, with a thesis focused on AI, specifically
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performance, yet their atomic-scale origin and role in reactivity remain poorly understood. The project addresses this open problem by integrating high-throughput Density Functional Theory, machine-learning
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with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
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to demonstrate documented proficiency in English. You have knowledge and expertise in computer vision and/or medical image analysis, deep learning as well as mathematics. You have substantial expertise in
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identification, optimization, or numerical methods is valuable, as is knowledge of data analysis and machine learning for complex, high-dimensional systems. Programming experience in MATLAB or Python, and an
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to contribute to one or more projects, learning advanced cellular and molecular biology and anaerobic microbiology techniques. The candidate’s day will be split between benchwork to generate data, and computer
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experience Practical experience in machine learning and the application of large language models Knowledge of OMICS and image data analysis A willingness to engage in interdisciplinary scientific work