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-equilibrium conditions. The project is a UKRI/NSF collaboration with Virginia Tech, and the use of direct numerical simulation, modelling and analysis will be complemented with experimental data from
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profound interest in inorganic chemistry, in both experimental and modelling applications. We are looking for candidates who are also interested in the analytical and numerical aspects of the work to support
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research frontier in computer vision that combines three critical challenges: class imbalance, recognition of rare and unseen species, and dense labelling of high-resolution imagery. The candidate will
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Advances in computing, experiments, and information will continue to reshape engineering in the next decade. This PhD position will nurture a multidisciplinary innovator with the tools to unravel
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application of computational tools for the early detection and deconstruction of chromosomal instability in cancer” For further information about the research group, including their most recent publications
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This self-funded PhD opportunity sits at the intersection of several research domains: multi-modal positioning, navigation and timing (PNT) systems, AI-enhanced data analytics and signal processing
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Systems, or a related field. Strong analytical and critical thinking skills. Strong machine learning (ML), computer vision (CV), large language models (LLM) for quantitative data, texts, images, and sensor
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Contract type: Full time (35 hours - 1FTE), fixed term (18 months) Detailed description The recruited candidate will be responsible for generating a database of qualitative data acquired through
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driving invasion. You will work closely with our partners in African countries where An. stephensi has been detected to co-develop and implement the analysis plan for their data sets on mosquito vector
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systems theory Excellent analytical and problem-solving skills Desirable criteria Advanced programming and data analysis skills Computational neuroscience background Behavioral data analysis skills Strong