231 coding-"https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" PhD positions in United Kingdom
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and deploy advanced deep learning and foundation models for surgical scene understanding segmentation, tracking, and operator assistance. You will write, test, and optimise Python and C++ code for real
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discipline *Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant. Expertise in atmospheric physics and chemistry Enthusiasm for coding of complex atmospheric
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‘Course Search’ to identify your programme of study: · Search for the ‘Course Title’ using the programme code: 8100F · Research Area: Chemistry · Select ‘PhD in Chemistry (full time) as
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: Comprehensive personalised Assessment, early Risk Evaluation and clinical management” (HER-CARE) project. The successful applicant will work on the project “Assessing the role of rare germline non-coding genetic
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experimental fluid dynamics. Experience with laboratory instrumentation, data acquisition, and coding for experiment control or data analysis (e.g. LabVIEW, MATLAB, or Python) would be beneficial. Applicants
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having good visibility of how the system and its parts respond to faults, events and trips (unlike the recent Iberian experience). For example, Statkraft is the proposer of Grid Code Modification GC0181
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IrO2. We are looking for a highly motivated independent learner with good coding skills to work with us at the interface of AI and electrochemistry. As a PhD researcher, you’ll work at the forefront
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data handling skills Understanding of data structures, data modelling and data management Ability to write robust, reproducible code in Python or R Experience writing academic publications independently
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of the candidate Essential requirements: A 1st class or 2.1 degree (or equivalent) in Environmental Science, Remote Sensing, Computer Science, Surveying Engineering, or related field Strong coding skills (Python
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workshops with carers or stakeholders, and qualitative data coding and content analysis, alongside conducting meta-systematic (umbrella) literature and policy reviews on caregiving experiences. A key aspect