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Artificial intelligence and machine learning methods for model discovery in the social sciences School of Electrical and Electronic Engineering PhD Research Project Self Funded Prof Robin Purshouse
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protein structural insight with hands‑on ML development: adapting and applying state‑of‑the‑art structure prediction and design frameworks, training/fine‑tuning models, and running scalable computational
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practice, emission spectra are acquired in order to infer the number density of certain excited states. We then run a CR model that solves the plasma kinetics as a function of a reduced number of free
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laboratory processing of root data Assist in ongoing field sampling campaigns Conduct basic data analysis Collaborate with PhD researchers from the modelling and experimental teams to link data and model
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ALMA MATER STUDIORUM - UNIVERSITA' DI BOLOGNA - - DIPARTIMENTO DI INGEGNERIA CIVILE, CHIMICA, AMBIENTALE E DEI MATERIALI | Italy | 19 days ago
. Structural optimization and numerical modeling. Where to apply Website http://www.unibo.it Requirements Additional Information Eligibility criteria to apply for research grants fill out the form available
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illumination variations, which introduce non-stationary shifts and degrade the performance of conventional models. The project proposes the use of hypernetworks to dynamically adapt the parameters of the gaze
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, designing, implementing, and evaluating ML models that address practical challenges across domains. The researcher will contribute to the development of a full machine learning pipeline, including data
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applications. Key Responsibilities: Develop and fine-tune computer-vision models, instance segmentation, and retrieval-based estimation from images and text metadata. Build and evaluate monocular depth pipelines
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change, biodiversity, and food security challenges contribute to advance cross-scale modelling approaches integrating economic, ecological and social dimensions assess market, technological, governance
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state‑of‑the‑art structure prediction and design frameworks, training/fine‑tuning models, and running scalable computational campaigns. Key responsibilities Design and execute in silico protein and