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outstanding candidates whose work lies at the intersection of statistics, machine learning, data analytics and modern AI algorithms. This includes, in particular, statistics for high-dimensional and complex
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of the candidate), in visualization and data analysis, cooperative systems, data mining and machine learning, education, didactics and entertainment computing, or Neuroinformatics. Across faculties, renowned
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team, your expertise in AI and other cognitive computing methodologies, such as but not limited to machine learning (ML), large language models (LLMs), small language models (SLMs), natural
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NOVA Institute for Medical Systems Biology (NIMSB) announces Four Independent Group Leader positions
for integration of large-scale omics datasets, and application of machine learning and statistical modelling for decipher cell and tissue behaviour, elucidate disease mechanisms, and enable patient stratification
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geological field-based methods and big data applications and machine learning methods. Research focus will be on feedback processes between erosion, sedimentation, tectonics and climate, and topics could
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an excellent scientific track record. Proven expertise in environmental genomics, metagenomics, or large-scale omics data analysis. Experience with machine learning or AI approaches in biological data is an
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to candidates from a broad range of AI subfields, including, but not limited to machine learning, generative AI, computer vision, representation and reasoning, natural language processing
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-Molecule Drug Discovery Strong foundation in computational methods (e.g. docking, molecular dynamics, QSAR, machine learning) Experience working across diverse target classes Demonstrated impact on
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clinical research center is a plus; Knowledge and experience of machine learning methods; Constructive attitude, flexibility, outgoing and service oriented; Excellent communication, negociation and
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computational modeling and/or analysis of complex biological systems, integrating state of the art tools such as machine and deep learning approaches. Experience in managing biological databases and statistical