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Field
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Sciences, Urban Climate, Architecture Engineering, Building Science, or related fields majoring in Computational Fluids Dynamics (CFD). . Candidate who is in the final stages of PhD studies (i.e. submitted
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Sciences, Urban Climate, Architecture Engineering, Building Science, or related fields majoring in Computational Fluids Dynamics (CFD). . Candidate who is in the final stages of PhD studies (i.e. submitted
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beyond the PhD in a research or engineering environment focused on large-scale AI. Experience with geometric deep learning, diffusion architectures, or related frameworks (e.g., OpenFold, AlphaFold2/3
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-level model stability, including mixed-precision training (BF16/FP8) and gradient accumulation. Preferred Qualifications At least two years of experience beyond the PhD in a research or engineering
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with fabrication engineers to translate physical processes into machine learning models, design and train deep learning architectures, and evaluate their ability to generalise across different process
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, spectroscopy, and electrical performance measurements. You will work closely with fabrication engineers to translate physical processes into machine learning models, design and train deep learning architectures
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Requirements PhD/Master’s in Naval Architecture, Ocean Engineering, Civil Engineering, or related field. Proficiency in hydrodynamic modeling tools (e.g., WAMIT, ANSYS AQWA, OrcaFlex) and finite element analysis
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and establish transgene-free lines Perform phenotypic and genotypic analyses of edited plants Investigate genes involved in plant architecture, biomass, flowering time, and fertility Engineer molecular
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Join a pioneering team shaping the future of advanced Battery Management Systems (BMS) for electric vehicles and energy storage. The post holder will be a member of the Architectural Engineering
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with fabrication engineers to translate physical processes into machine learning models, design and train deep learning architectures, and evaluate their ability to generalise across different process