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Bugging Out: When AI loses the Plot – Detecting and Taming Hallucinations in LLM-Generated Code (S3.5-COM-Wang)
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BBSRC Yorkshire Bioscience DLA Programme: The role of long non-coding RNAs in guard cell function and development
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Search’ to identify your programme of study: search for the ‘Course Title’ using the programme code: 8208F leave the ‘Research Area’ field blank select ‘PhD Energy Materials' as the programme of study You
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select ‘Create a Postgraduate Application’. Use ‘Course Search’ to identify your programme of study: search for the ‘Course Title’ using the programme code: 8208F leave the ‘Research Area’ field blank
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of the most persistent challenges in modern software development: "flaky tests"—automated tests that pass and fail non-deterministically without changes to the code. In this role, you will bridge the gap
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solutions, develop machine learning/AI pipelines and deploy these pipelines in production. You can demonstrate experience in programming languages (e.g., Python, R, C++), software engineering (e.g., coding
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code: 8208F · leave the ‘Research Area’ field blank · select ‘PhD Energy Materials' as the programme of study You will then need to provide the following information in the ‘Further Details
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neutrino physics—using a combination of CMB and large-scale structure data. The analysis will rely on modified Boltzmann codes (e.g. CAMB or CLASS) and Monte Carlo techniques for parameter inference
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Finance team. In addition, the role holder will also contribute to finance tasks such as updating the finance system, coding expenses claims and amending payroll coding. You will have previous research
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are compliant with current statutory/safety requirements and approved codes of practice. You will deliver a professional, customer-focussed electrical engineering support service to all stakeholders