45 parallel-computing-numerical-methods-"https:" PhD positions at Technical University of Denmark
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with the experimental group of Andras Kis at EPFL. Responsibilities The successful candidate will develop and apply ab initio computational methods rooted in many-body (perturbation) theory to explore
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, experimental and computational methods to study the physiology of microbes. You will work in a team contributing to projects of other team members, while independently developing your own project. You will also
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doctoral candidate who meets the following requirements: A background and strong interest in aluminum alloys, fatigue analysis, and numerical modelling is preferred. Experience with computer aided design and
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the following areas: Knowledge of computer science and machine learning. Familiarity with electrical and electronic engineering. Proficiency in programming languages such as Python, C++, or MATLAB. Experience
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learning potentials (MLPs). You will develop geometric graph manifold learning methods and utilize those to learn from heterogeneous quantum mechanical datasets. You will implement and benchmark state
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interest in cement chemistry and/or alkali-activated materials is a plus Experience with thermodynamic modelling and characterization methods like quantitative XRD is a plus The ability to work independently
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. To optimize the controllability of cold storage using phase change materials, a novel charge determination method (measurement of energy stored) will be developed in the laboratory. You will work at the
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at DTU. For information about our enrolment requirements and the general planning of the PhD study programme, please see DTU's rules for the PhD education . Assessment The assessment of the applicants will
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for grid support to future power grids and quantify the performance of the developed control. Your work will involve both theoretical analysis and simulation-based validation of the proposed control method
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with both academic researchers and industry experts across Europe. Your key responsibilities will include: Conduct analysis of polymer membrane waste materials Develop electrospinning methods to produce