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Polymer manufacturing is highly energy intensive, and achieving net zero requires more than fuel switching. This project focuses on process systems engineering for industrial decarbonisation
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with a background in theoretical physics, statistical, soft or biological physics Experience with using inference/machine learning tools and basic programming is a plus As a university, we strive for
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ARDITI - Agência Regional para o Desenvolvimento da Investigação Tecnologia e Inovação | Portugal | 2 months ago
of the Board of Directors, hereby announces the opening of an international selection procedure for the recruitment of one Junior PhD Researcher to develop the scientific and technological activities foreseen
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areas of nanoscience and nanotechnology. Job Title: PhD STUDENT (INTRANASAL TREATMENT AND IMAGING OF NEUROLOGICAL DISORDERS USING NANOPARTICLES) Research area or group: Nanostructured Functional Materials
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are burned for the production of energy. Indeed, during the extraction process of cellulose, hemicelluloses are degraded and solubilized in the effluent of the process, called, the black, liquor. This liquor
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» Maritime engineering Engineering » Computer engineering Computer science Architecture » Naval architecture Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 23 Aug
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computational image analysis, computer vision and machine learning. The aim is to develop robust and standardized methods to link structural, mechanical and biological properties to biomaterial performance and
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processes which result in the formation of a soft and smooth bolus ready to pass down the oesophagus into the stomach. These perceived changes in texture during oral processing not only affect our enjoyment
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parking spot. Selection process The recruitment process consists of three stages: a) selection of candidates by the Committee based on the alignment of the candidate’s competencies with the requirements
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, preferably in Python, and experience with machine learning or deep learning. Experience in computer vision, digital pathology, whole-slide image analysis, self-supervised learning, foundation models, multiple