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recognition, and enable seamless collaboration between humans and machines. Long-Term Human-Technology Evolution: investigate the longitudinal impact of human-technology interaction on learning, behavior, and
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administration and organisation. We are looking for a/an University assistant predoctoral/PhD Candidate 51 Faculty of Physics Startdate: 01.04.2026 | Working hours: 30 | Collective bargaining agreement: §48 VwGr
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Ability to lead and work in teams Essential Application/Interview Experience and capability in blast computational simulations using codes such as Viper:: Blast, machine learning, and/or LS Dyna Desirable
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-cell transcriptomics, or spatial tissue profiling data, and are keen to develop new methods, for example using machine learning. You have a proven track record of independent research funding and high
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bioinformatics for immunology research programs. You'll work at the cutting edge of AI-enhanced immunology, applying deep learning, foundation models, and advanced machine learning approaches to understand how
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Project description Electromagnetic (EM) sensing is emerging as a powerful enabling technology for modern high-value manufacturing. Advances in computing power and machine learning now allow us to
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Sessional Lecturer - PPG2012H-S-Topics: Applied AI Systems & Governance: Technology, Policy & Practi
-world policy applications to equip students with the knowledge and tools needed to engage with AI at both strategic and operational levels. Students will learn how modern machine learning models work
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» Autonomic computing Engineering » Maritime engineering Technology » Computer technology Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 25 Apr 2026 - 23:59 (Europe
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protein coding genetic association data with functional and machine learning-derived features 4. Developing methods to characterize the genetic architecture of autism Salary and Benefits This position is
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the development and application of probabilistic inference methods and machine learning techniques for quantitative uncertainty modeling and for the integration of heterogeneous climate data