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(CFD) methods. The role focuses on transitioning legacy CFD solvers to modern, Python-based AI frameworks (e.g., PyTorch, JAX), targeting high-efficiency, scalable simulations of water-air systems with
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on the development of new synthetic approaches for porous materials-coated bacteria. Perform detailed structural characterizations of obtained systems using experimental and theoretical methods. Evaluate
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). Hands-on experience with computational materials methods (e.g., DFT, molecular dynamics, phase-field simulations). Proficiency in Python, TensorFlow/PyTorch, and scientific computing. Experience in
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in Health Research Methods, Epidemiology, Computer Science, or a related field. Expertise in evidence synthesis and clinical guideline methodology such as network meta-analysis, GRADE. Experience with
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dynamics, bubble formations, and trader behavior. Using quantitative methods, econometrics, and statistics, we aim for innovative insights into asset market mechanics. In this role, you'll manage research
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data and electronic health records Contributing to phenotype development based on high dimensional and free text data leveraging advanced methods such as large language models. Analysis
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) who is highly skilled in and deeply passionate about computational electromagnetism and mathematical physics/engineering. The SRF should have strong background in computational methods for solving
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in Bioengineering/Materials Science/Chemistry Strong experimental skills, especially in self-assembled monolayers, surface fabrication and characterization, and associated methods Excellent
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analytical biochemistry/chemistry or bioengineering, who will be tasked to conduct in depth biochemical characterization of alternative proteins and peptides using analytical methods such as high-performance
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Prepare manuscripts, reports and presentations Guide and train students and junior lab members Maintain complete documentation for modelling methods and analysis used Regular communications with supervisors