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international conferences and journals. Willingness to participate in international networking and travel is expected. Candidates already holding a PhD within this field are not eligible for this position. As per
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assist in preparing competitive grant applications for internal and external bodies. Supervision & Mentorship (10%): Co-supervise Master’s and PhD projects, and mentor undergraduate/postgraduate research
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technical ideas, and engaging with GenAI industry and open-source communities. Job Responsibilities: Conduct the research in AI/ML domains, especially in probabilistic ML and scalable sequence network
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Applicants should possess: • A PhD (completed or close to completion) in Communication, Public Health, Information Systems, or a closely related discipline. • Strong quantitative research
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, and hypothesis generation. Integrate transcriptomic, epigenomic, spatial transcriptomic, and other multi-omics datasets. Perform gene regulatory network, transcription factor, pathway, and cell-state
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leadership training, as well as career development support • Joining ISTA’s and the NOMIS Foundation’s global networks of exceptional, pioneering researchers Proposals are invited in the following fields
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• Scientific integrity and leadership training, as well as career development support • Joining ISTA’s and the NOMIS Foundation’s global networks of exceptional, pioneering researchers Proposals are invited in
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interdisciplinary collaborative research, and looking for a PhD position, this opportunity can be for you. The Energy Informatics EI@ND Networks and Distributed Systems group at the Department of Informatics (IFI
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the correctness, robustness and reliability of deep neural networks and AI-enabled software systems. Job Responsibilities: Conduct research in adversarial machine learning, AI security and the robustness of deep
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the correctness, robustness and reliability of deep neural networks and AI-enabled software systems. Job Responsibilities: Develop novel methods and algorithms for the verification, testing and robustness analysis