603 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "U.S" "U.S" positions in Norway
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Value conflicts and tensions in the life sciences and its different epistemic cultures and evaluative principles and practices. For more information and how to apply: https://www.jobbnorge.no/en/available
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academic environment. The person appointed will primarily teach and supervise students at bachelor's, master's and doctoral levels within the subject area, conduct research and disseminate research results
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for this position is on campus in Bergen, and full presence is expected. Work tasks: Teaching and supervision, including examination. Shared responsibility for learning resources in accordance with study programs
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the semantic foundation that enables AI systems to reason more coherently about ship designs, reducing ambiguity in the data available to machine‑learning systems, and supports explainability by grounding AI
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, including individually tailored career development plans with formal supervision and project-based learning. Secondments, consortium meetings, and workshops will provide hands-on experience in collaborative
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-state model will be approximated using machine-learning surrogates and will be used for a real-time optimization, such that the plant operates optimally despite disturbances. The candidate will be part of
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PhD Research Fellow in Theoretical and Computational Active Matter Physics for Glioblastoma Invasion
Language requirement: Good oral and written communication skills in English English requirements for applicants from outside of EU/ EEA countries and exemptions from the requirements: https://www.mn.uio.no
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event, see details at https://uit.no/arcticmsca/programstructure ). At this event, the candidates will present their past research achievements, discuss future plans with their potential supervisor and
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physical/digital event, see details at https://uit.no/arcticmsca/programstructure ). At this event, the candidates will present their past research achievements, discuss future plans with their potential
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. The core research objective of this PhD is to design and evaluate “latency hiding” methods for immersive networked interactions. This involves (i) developing predictive machine learning models that forecast