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apply statistical and machine learning models to identify predictive markers of depression. Design and execute experimental protocols related to self- representation and chronic pain. Prepare manuscripts
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an opportunity for renewal to perform research using artificial intelligence (AI) and machine learning (ML) with a focus on large language models (LLMs) and foundation models (FMs) relevant to electric power
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from varied sources, and machine learning methodologies. The underlying data are complex and will require sophisticated data management and integration skills. A candidate should have proficiency with
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. Experience with machine learning is highly preferred. Ability to work independently and as part of a team. Key Requirements for PhD: Hold a Bachelor's degree with outstanding performance in Physics or related
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: This project is related to machine learning for Urban Informatics. In this context, the intersection between the urban infrastructure and digital technologies plays an essential role. The aim is to develop
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Post-doctoral Researcher in Multimodal Foundation Models for Brain Cancer & Neuro-degenerative Disea
Qualifications PhD in machine learning, computer vision or a related field. Established expertise in deep learning methods applied to images analysis. Experiences with generative models, volumetric image
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, United States of America [map ] Subject Area: Computational Science / Artificial Intelligence/Machine Learning Appl Deadline: (posted 2025/11/19, listed until 2026/01/26) Position Description: Apply Position Description
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06.12.2021, Wissenschaftliches Personal The professorship of Data Science in Earth Observation is seeking six new PhD candidates/PostDocs for its new center for Machine Learning in Earth Observation
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& Machine Learning not excluding other modeling frameworks) of safety critical socio-technical infrastructure systems. The candidate will be primarily responsible for writing and submitting refereed journal
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psychoactive substances, in seized drug products or clinical samples. The candidate will have the opportunity to work directly with experimentalists to validate predictions made by their machine-learning models