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Campylobacter disease burden is assessed, identify drivers of change and possible interventions. You will explore how genomic diversity relates to clinical outcomes, whether machine‑learning approaches can
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next generation sequencing, multi-omics analyses, and advanced machine learning to diagnose the disease and identify its underlying subtype from a simple blood draw. This approach could reduce diagnosis
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imaging. Experience with scientific programming (e.g., MATLAB, Python and/or C++). Excellent analytical and problem-solving skills. Interest in image reconstruction, beamforming, machine learning, and
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, engineering, and the broader questions of how AI systems affect the people and institutions that interact with them. You should bring: A master’s degree in computer science, machine learning, HCI, or a related
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spatiotemporal patterns of gene expression associated with these diseases through the development and application of machine learning methods for data analysis and integration. The thesis will be carried out in
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The International Institute of Molecular Mechanisms and Machines Polish Academy of Sciences | Poland | about 1 month ago
3 Aug 2026 Job Information Organisation/Company The International Institute of Molecular Mechanisms and Machines Polish Academy of Sciences Research Field Biological sciences » Biology Researcher
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20 Aug 2026 Job Information Organisation/Company Eindhoven University of Technology (TU/e) Research Field Engineering » Computer engineering Engineering » Electrical engineering Researcher Profile
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. Experience with finite element software development. Experience with machine learning and data-driven modelling. Experience with high-performance computing. Previous scientific publications. Qualification
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learning, computer vision, or a related field; knowledge of affective computing, generative AI models, and deep-learning methods; proficiency in Python and experience with machine-learning libraries
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companies. Hybrid & Data-Driven Modeling: Apply machine learning and hybrid physics-AI approaches to model industrial systems, accounting for physical constraints, sensor noise, and heterogeneous datasets