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the direction of the Principal Investigator in building a first-of-its-kind Software as a Medical Device (SaMD) that predicts, detects, and manages SSIs by fusing RGB + thermal wound images
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already been awarded a PhD degree. Selection process You should submit your CV through a dedicated site: https://cv.newton-6g.eu/ Additional comments Position: Data-driven models for CF networks
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manufacturing technologies and eager to develop and build experimental setups and combine this with physics-based modelling? Join us as a PhD candidate and contribute to making volumetric 3D printing predictable
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integrating modeling, machine learning (ML), and advanced control methodologies. The research will focus on designing AI-driven algorithms to assess battery health, predict degradation trends, and optimize
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effectiveness. Integrate FDD and maintenance outputs with digital twins, predictive control frameworks, and operator decision support systems within FLARE. Plan, coordinate, and participate in industrial site
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predicting pollutant dispersion in complex environments like industrial sites remains difficult due to fluctuating wind conditions and obstacles. This PhD project offers a unique opportunity to develop
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interactions. This involves (i) developing predictive machine learning models that forecast user actions and remote system responses across audio, video and haptic modalities, and (ii) jointly orchestrating
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industrial decarbonisation modelling to support the EU-funded FLARE project. The role will lead the technical development and integration of bottom-up, organisation-level decarbonisation models for energy
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to shape disease risk. Yet most clinical risk models ignore this exposome. In BEE, we will build explainable, physics-guided, GeoAI-driven models that: Predict acute and chronic NCD risks at the population
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, as well as from industry. The successful candidate will work in the established collaboration between DSB and ICGI to develop multimodal deep learning models for predicting prostate cancer