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model or machine-learning-enabled assets at a company or University). Basic understanding of early-stage technology development. Knowledge of basic principles of intellectual property and licensing
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for Catalysis and Organic Chemistry at the Department of Chemistry. The group has extensive experience in computational modelling, reaction mechanisms, and machine learning for catalyst design and discovery. Nova
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) signal processing, machine/deep-learning and computational linguistics. The team mobilizes them to produce methodologically sound research in response to some of the challenges posed by the nature and
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application of machine and deep learning models. The balance between experimental and computational method development will depend on the candidates’ profiles. Start date is expected to be 1 June 2026 or as
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member of a team. Have good knowledge of public transit system or willingness to learn and teach. Be able to use computer to record services or be willing to learn. Preferred Qualifications Peer training
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qualitative and quantitative analytical methods to model clinician attention, verbal reasoning, and documentation behaviour Develop and evaluate machine learning models, including unimodal, fusion, and
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. Expert knowledge of data modeling, statistical analysis, machine learning, and optimization techniques. Expert in leading and executing complex, high-impact data analytics projects and initiatives
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scoping to deployment and monitoring of production-grade models—with a focus on both Generative AI and Deep Learning. The ideal candidate holds a Ph.D. in Deep Learning or Generative AI and brings a strong
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interdisciplinary work at the interface between neurodegeneration, modeling, screening and machine learning Prior experience in iPSC modelling and CRISPR tools Our Offer A stimulating, international
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research, optimization, and decision analytics. • Supply chain engineering and logistics systems. • Human factors and ergonomics. • Data analytics, artificial intelligence and machine learning