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disconnectivity in brain networks relates to symptom networks and recovery trajectories in psychiatric patients. Apply and further develop methods from network science, machine learning, and computational
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blood samples to advance patient care. This role will involve developing computational models (statistical, machine learning, etc.), and using them to perform high throughput analysis of clinical data
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The project will prioritise digitising these records using natural language processing (NLP) and machine learning (ML) to create structured datasets. These will support AI applications in paediatric care
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PhD Research Fellow in ML-assisted reservoir characterization/modelling for CO2 storage (ref 290702)
-build ups in potential multi-site storage licenses. The research will help to suggest best practices for machine learning integration in de-risking CO2 storage sites. We seek a candidate with a strong
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para Ciência e Tecnologia; - PhD in Electrical Engineering, Computer Science, or equivalent scientific areas - Excellent academic and practical background in machine learning, deep learning, natural
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Your Job: We are looking for a PhD student in machine learning to work within a project linked to the “Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE)”. Your Job: Develop 3D+t
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technical specifications. Knowledge, Skills, and Abilities: Advanced applied statistics skills, such as distributions, statistical testing, regression, etc. Professional experience developing machine learning
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Group, https://www.chalmers.se/en/departments/e2/research/communications-antennas-and-optical-networks/communication-systems/ , employing around 45 people, including 8 faculty and 25 PhD students. During
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(IST) Experience Early Skills and Qualifications PhD in Physics, Electrical and Computer Engineering Degree PhD Employment Type Full Time About the position A research position for one Research Grant
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through applied research programmes. Faculty in the ICT Cluster undertake funded industry-relevant research, teach courses in Computer Science, Computer Engineering, Information Security and Software