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Engineering (Spanish MECES level 3 or EQF level 7), or any other official Master’s degree with equivalent content. PhD in Computer Engineering (Spanish MECES level 4 or EQF level 8) or any other PhD programme
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of California and the University Professional and Technical Employees (UPTE). The current bargaining agreement manual can be found at: http://ucnet.universityofcalifornia.edu/labor/bargaining-units/rx/index.html
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postdoctoral researchers, starting 1 January 2027 or soon thereafter. The positions are available for a period of 3 years and available for candidates with a PhD from another university than Aalborg University
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Website https://www.academictransfer.com/en/jobs/362527/postdoc-circular-and-multi-mate… Requirements Specific Requirements We are looking for applicants who hold: A PhD degree in Materials Science and
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and Experience The candidate must have a PhD degree in Microbiology, Genetics, Molecular Biology, or a related field by the position’s start date. Experience in microbial genetics and cultivation
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incorporating current trends and best practices in design. How to Apply: Interested candidates may apply online at https://jobs.coastal.edu/postings/122227 . Applicants must submit a cover letter, resume, design
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one or more of the following areas is meriting: Bayesian statistics, mathematical modelling, probabilistic machine learning, deep learning, large language models. Rules governing PhD students are set
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actively on the preparation and defence of a PhD thesis in the field of explainable reinforcement learning (XRL). Explainable reinforcement learning aims to make decisions, policies, and learning processes
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actively on the preparation and defence of a PhD thesis in the field of continual reinforcement learning. Continual reinforcement learning studies how agents can learn across a sequence of changing tasks
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well as resource limitations. 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