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Position Description The project will focus on the development and application of advanced data-analysis techniques for gravitational-wave science, including machine learning and deep learning
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, engineering or physics. Knowledge: Computational programming, machine learning, quantum transprot, device simulation. Professional Experience: use of device simulation codes applied to 2D materials. Personal
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contribute to project reporting. Work with the interdisciplinary team of the group (theory, computation, machine learning) and support junior researchers on SOT-related topics. Requirements: Education: PhD in
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to Principal Investigator level. Research areas include: Population neuroscience and multimodal neuroimaging Computational psychiatry and imaging genetics AI and machine learning for brain science Multi-omics
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CIC bioGUNE (Asociación Centro de Investigación Cooperativa en Biociencias) | Bilbao, Pais Vasco | Spain | 3 months ago
-based approach with a machine learning model (foundational model) to propose protocols for the sequential induction of transcription factors to generate desired cell subtypes. The selected candidate will
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fellows will receive joint mentorship from leading experts in metabolic biology, AI and machine learning, drug delivery, and translational medicine, while maintaining full academic independence in research
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exploration and etc. We are seeking distinguished scholars who will teach at UCAS while conducting research at CAS institute platforms—focusing first on over 20 key areas. Early-career scientists worldwide
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of core algorithms for image processing and computer vision based on deep learning and traditional algorithms; including but not limited to image recognition, detection, segmentation, generation
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Research Engineer - Tools developer for LSQUANT platform (Theoretical and Computational Nanoscience)
Personal Competences: Demonstrated competitive ability in using DFT simulations, and machine learning techniques and DFT. Demonstrated strong coding skills and a passion for UX/UI design. Summary
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parameters affect material properties and functional performance, and interacting with machine-learning and modelling teams to translate experimental results into predictive datasets. Preparing reproducible