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Field
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, wearable physiological sensing, and machine learning to uncover how factors like fatigue and cognitive workload impact technician performance. Join us to develop predictive models that predict human error
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modelling tools. This PhD position aims to achieve to develop by the use of automatic picking, rather than manual, travel time picks, and the application of machine learning methods to reliably pick relevant
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Signal Processing and Image Analysis group (DSB), Section for Machine Learning, at IFI. DSB has seven full-time and five adjunct positions and carries out research across image analysis and machine
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PhD qualification degree in Electronic Engineering or Computer Science Familiarity with pinching antennas and machine learning Good written and oral communication skills Proficiency in python
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reproducible analysis workflows Familiarity with computational models of vision and machine learning methods (for example CNNs, deep generative models, encoding models) is preferred but not required Ability
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that delivers real-time, hyperlocal information on urban heat risks in tropical cities. Leveraging Doppler lidar–based microclimate studies and machine learning, the research emphasizes vulnerable groups
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Research Infrastructure? No Offer Description Based at Australian Institute Machine Learning (AMIL), City East Campus 2 x Full-time, 2-year fixed term contracts Salary Range: $114,917 - $135,932 per annum
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computational electromagnetics and electromagnetic simulation techniques. Experience in AI-based RF transistor modelling is highly desirable. Solid knowledge of machine learning algorithms and their application
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Torbein Kvil Gamst 26th April 2026 Languages English English English Faculty of Science and Technology Postdoctoral Research Fellow in Machine Learning Apply for this job See advertisement
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dissemination, and translational opportunities Job Requirements: PhD in Chemistry with a focus on computational/peptide/organic/machine learning or a closely related discipline At least one first-author