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these fields is not required, what we are looking for is a strong motivation to learn, adapt, and apply these technologies to advance microbiome research. ProfileWe are looking for a driven and creative PhD
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of predictive models for energy demand and production. These models will leverage techniques such as time series analysis and machine learning and will be integrated into a digital twin platform. The aim is to
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to form your job content and to continuously learn through our VUB learning platforms and training courses; Interested? Then apply, at the latest on 30/05/2025 and upload the following documents
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models combining machine learning, and physics-of-failure (PoF) approaches using in-situ data • You work on projects independently • You will present your work at international conferences and
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, criterion handling and machine learning. Topic The main research objective is to contribute to the development of responsible AI, with a strong focus on trust and confidence handling when dealing with data
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are curiosity-driven especially interested in interdisciplinary research, eager to continuously learn with critical thinking and explore new ideas while collaborating with a team Language Skills: Fluent written
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experience with scientific computing, data analysis, machine learning and/or AI You have an interest in environmental sustainability and pharmaceutical production Considered a plus: You have experience with
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interest to acquire those in exercise physiology, metabolic & inflammatory regulation, molecular biology and biochemistry. Experience with basic molecular biology techniques, such as protein/mRNA extraction
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Intelligence (AI). Despite recent advances in data-based AI (e.g., deep learning and LLMs), knowledge-based methods are still state-of-the-art when it comes to building reliable and explainable decision support
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that he/she can check approve your file . You will have to upload some documents Proof of enrolment as a PhD student at your home University A signed Learning Agreement. Please chose the correct template