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Field
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modern Bayesian modelling frameworks such as Stan, Turing.jl, and PyMC, including automatic differentiation frameworks, MCMC sampling algorithms, and iterative Bayesian modelling. Special attention will be
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novel sensing approaches to combine with machine learning algorithms to solve real-world problems in food manufacturing. You will have sound knowledge in electronic engineering, embedded systems design
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degree in Computer Science, Math, Statistics or Engineering programs. Ph.D would be an asset. Knowledge of classification algorithms, vision-language models (VLM) and Hugging Face Transformers is an asset
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our software development team, developing novel scientific algorithms and applications in the areas of spectroscopic analysis and mining of the science data catalogues extracted from the pipelines
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are different downwind of locations where an aircraft has passed through ice-subsaturated air (but where no contrail was formed). What we are looking for: Research Associate: Hold a PhD in Aerospace Engineering
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Liverpool where, in the School of Computer Science and Informatics, we have an active group of PhD students, postdocs, and academics working at the intersection of Machine Learning, Verification and
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stakeholders. Demonstrated ability to manage iterative feedback cycles and refine deliverables accordingly. The skills, qualifications and experience required to perform the role are: A PhD in Computer
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PhD in Civil Engineering or a relevant related field, or have equivalent industrial experience. Chartership with a Professional Engineering Institution (CEng or equivalent) is desirable. Appointment
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-subsaturated air (but where no contrail was formed). Research Associate: Hold a PhD in Aerospace Engineering, Atmospheric Science, Physics, or a closely related discipline*, or equivalent research, industrial
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work under the joint scientific mentorship of Dr Sabrina Tosi (Dr Sabrina Tosi | Introduction | Brunel University London ) and Dr Cristina Pina (Cristina (C) Pina MD PhD - Cristina (C) Pina MD PhD