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with disabilities through inclusive Human-Computer Interaction (HCI) and participatory design methods. This PhD project will focus on methods and experiences in co-designing with stakeholders, especially
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Social Participation). TACIT focuses on the co-design of inclusive and accessible AI and other technologies for people with disabilities through inclusive Human-Computer Interaction (HCI) and participatory
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each other. This necessitates a multidisciplinary approach bringing together optimization, machine learning and behavioral modeling methodologies. In FlexMobility we propose a holistic approach to design
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unique opportunity to contribute to the technological foundations for tomorrow’s machine learning. Your job In the ERC project FoRECAST, we aim to develop theory (e.g., new probabilistic and differential
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of an 8-year program on circular battery technology. The project aims to develop new-generation innovative and circular battery packs for heavy-duty trucks, busses, mobile machines, maritime and
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analysis, has good software skills (Python, C++, ROOT) and has (some) research experience in experimental particle physics. Experience with machine learning algorithms and software is desirable but not
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will include boosted final states. The analysis strategy is strongly focused on advanced machine learning techniques, developing attention-based transformer models that will directly classify events from
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University Medical Center Utrecht (UMC Utrecht); yesterday published | Netherlands | about 2 months ago
, epidemiologists, clinicians and lab researchers, with expertise in the field of prediction modeling, longitudinal data analysis, statistics, data science, machine learning, AI, organoid models and cystic fibrosis
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explore how artificial intelligence can be used to predict the response to therapy in cases of acute rejection of the kidney graft. Using machine-learning techniques, you will assess kidney transplant
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considered an advantage. Technical Skills Proficiency in MATLAB, Python, and/or R. Experience with data science frameworks (e.g., AI, LLMs). Familiarity with machine learning (e.g., scikit-learn, MVPA, RSA