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Field
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: The doctoral candidate will perform computational analysis of a combination of multi-omics data from the gut microbiomes of patients with Alzheimer's disease or Parkinson's disease. The candidate will use state
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interaction with the other PhDs/Postdocs and more senior scientists within the MicroAM project. Responsibilities and qualifications If you are interested in numerical simulations of advanced manufacturing
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Master’s degree (or equivalent) in mathematics, computer science, physics, or related field. Sound knowledge in (scientific) machine learning, and knowledge in numerical analysis and numerical linear algebra
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) in an appropriate discipline. Subject Area Computer Science & IT, Electrical & Electronic Keywords Artificial intelligence, floating-point arithmetic, numerical analysis, computer arithmetic, machine
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of new EEG and MEG neuroimaging and mc-tCS simulation approaches based on realistic head volume conductor models using modern finite element methods as well as sensitivity analysis. The new methods will be
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aluminium in high-value products produced by mega-casting. The main objective of the PhD project is to develop a finite element analysis (FEA) framework that can accurately predict the mechanical properties
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scientific programming and numerical / statistical analysis of simulated and observed data. Candidates should be able to demonstrate motivation and a strong eagerness to learn, and have the ability to both
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Foundation (DFG). This PhD project is dedicated to numerical modelling of WMT processes in the Skagerrak by applying Eulerian and Lagrangian analysis methods to quantify the mixing responsible for the WMT
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spaces using PET analysis Conducting literature review, writing scientific publications, and presenting research results Mentoring student courses in radiochemistry and reactive transport Your profile
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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