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with low-data, sparse, or noisy datasets, typical in early-stage drug discovery. Technical skills: Proficiency in Python (required). Practical experience with machine learning or deep learning workflows
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designers of their workplace rather than passive recipients of noise measurements. The research will follow an iterative research and development process characterized by deep, on-site engagement with NPICU
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physics and permeability evolution models from µCT data using machine learning and computational tools (PuMA/CHFEM/MOOSE) validated against experimental observations Bridging scales from pore-level
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. Upwelling events bring nutrient rich deep-water to the surface, promoting phytoplankton production through communities dominated by known producers of omega-3. As such, these areas could represent hot-spots
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for a candidate with: an MSc in computer science, artificial intelligence or a related field a creative and collaborative mindset strong programming skills in Python or Rust strong skills in deep learning
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creative and collaborative mindset strong programming skills in Python or Rust strong skills in deep learning systems strong analytical and problem-solving skills fluency in English, both written and spoken
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/phd-in-micromechanical-experime… Requirements Specific Requirements An outstanding, motivated, enthusiastic, curiosity-driven researcher. Deep analytical skills, initiative, creativity, and flexibility
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creation that controls clogging patterns Developing predictive digital rock physics and permeability evolution models from µCT data using machine learning and computational tools (PuMA/CHFEM/MOOSE) validated
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investigate deep learning methods for local data augmentation and adaptive point density control, addressing the anisotropy and uneven sampling typical of urban LiDAR. You will work on a four-year doctoral
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An outstanding, motivated, enthusiastic, curiosity-driven researcher. Deep analytical skills, initiative, creativity, and flexibility are highly desired. An MSc degree in Mechanical Engineering, Materials Science