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Field
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interdisciplinary collaborations. Leading-edge platforms including High Performance Computing, computational and cutting-edge data analytics methods, and growth and retention of vital capacity in research software
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-scale foundation models, especially pipeline/model parallelism. Track record of creating HPC software for numerical methods. Domain expertise in areas like computational fluid dynamics, material
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advanced many-body methods, high-performance computing, and machine learning approaches. The successful candidate will play a leading role in developing computational methods and high-performance algorithms
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of numerical methods Geophysical fieldwork experience, preferably with GPR and EMI Strong English writing skills Since the work involves interdisciplinary cooperation with several researchers and technicians
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network, including 4 training schools and two workshops. As a participant of the project, the PhD student will become part of a team at DTU with numerical and experimental expertise in photonic computing
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steady and transient state, at scales ranging from nanometres to millimetres. Develop numerical methods to capture droplets evaporative behavior accurately Compare and validate numerical results with
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edge of energy systems and computational engineering, developing scalable methods to simulate and secure IBR-dominated grids. Your key responsibilities include: Conducting large-scale simulations
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quantitative image analysis, numerical modeling, and explainable AI (XAI) with state-of-the-art biophysical methods. Using techniques such as traction force microscopy, microfluidics, 3D bioprinting, and
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experience Demonstrated programming expertise in MATLAB and/or Python (object-oriented design, numerical methods, scientific visualization) Prior experience in scientific computing or within the subsurface
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of Architecture and the Built Environment), where you will collaborate closely with a parallel PhD project within the Faculty of Aerospace Engineering focused on meshfree numerical methods. Together, you will work