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Field
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these challenges by advancing machine learning (ML)-based and data-intensive methods for the scalable design, processing, and deployment of DPPs. The advertised PhD position focuses on developing ML-based and
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into the AI development lifecycle to build more resilient systems from the ground up. What We Are Looking For For PhD Candidates: An MSc degree (or equivalent) in Computer Science, Cybersecurity, Machine
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(or equivalent) in Computer Science, Machine Learning, Mathematics, or a related technical field. For Postdoctoral Fellows: A completed PhD in one of the fields mentioned above and a strong publication record
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physics data analysis, machine learning, and interactive and collaborative systems. The prospective PhD candidates will work in close cooperation with our current PhD students within the PhD programme, and
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position The University of Agder invites applications for a full-time fixed-term appointment as PhD candidate in Technology/Information and Communication Technology for a period of three years with a
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Data Science, Human Centered AI, and the SLAC Machine Learning Program. KIPAC also has strong ties and active collaborations with theorists at LITP @ Stanford and SLAC. The positions offer a competitive
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training deep learning models on biological, chemical or related datasets Proficiency in Python for data science and machine learning Possess sufficient breadth or depth of specialist knowledge with deep
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publication record. Outstanding data analytics, mathematical, and computer modelling skills. Excellent interpersonal communication and oral presentation skills in English Self-driven and strong team spirit Open
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machine learning methods to the analysis of large-scale astronomical datasets, with a particular emphasis on time-domain astronomy. Research directions will be flexible and shaped according to mutual
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knowledge of syntax-based statistical analysis tools. Strong skills in developing reproducible and transparent analysis workflows. Solid background in machine learning and analysis of large and complex