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archaeological and historical contexts is also required. Additionally, the ability to perform *ad hoc* data processing (multivariate statistics, machine learning, etc.) is desirable. Proficiency in programming
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applicability. By bridging machine learning, behavioural science, and clinical research, the project seeks to establish foundational methods for trustworthy agentic AI systems that can be deployed across diverse
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projects involve large-scale population cohorts, single-cell genomics, statistical genetics, functional genomics, machine learning, and clinical translation. We are a diverse and international team
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IOCB Prague (Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences) | Czech | 2 months ago
bioinformatics or molecular biology position. We're looking for someone genuinely excited by the intersection of structural/molecular biology, physics-based simulation, and machine learning/deep learning – a
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motivated students with a strong background in engineering or computer science. The ideal candidate will have: Strong programming and software skills. An awareness of machine learning theory and techniques
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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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assurance, data science, data-driven modelling, digital manufacturing workflows, and Digital Product Passport B.3 Hands-on experience in machine learning, ontologies and knowledge graphs, IIoT and digital
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problems, numerical mathematics, optimisation, machine learning and imaging physics, with applications ranging from medical and industrial imaging to geophysics. For more information, please visit
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/machine learning tools and basic programming is a plus As a university, we strive for equal opportunities for all, recognising that diversity takes many forms. We believe that diversity in all its
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to correlate polymerisation kinetics, macromolecular architecture, morphological evolution and drug encapsulation mechanisms. Beyond experimental work, the project will integrate machine learning approaches