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- UNIVERSITY OF VIENNA
- University of Sheffield
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Field
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Experience Experience developing research software using appropriate languages and environements (Python, Julia, Matlab) Knowledge of optimisation problem formulations and solution methods Experience of risk
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the development of good programming skills (Python/MATLAB or similar), good communication skills, the ability to work independently, and a willingness to engage with industrial partners. To apply, please contact
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driven research Maths competency and experience with statistics in research Software competency: ImageJ, Matlab, Graphpad Prism, R Evidence of Github use An interest in cardiovascular physiology
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substantial mathematical content. Skills in programming (MATLAB, Julia) and numerical bifurcation theory (e.g. DDE-BIFTOOL, MATCONT) are desirable. Background knowledge in one or more of dynamical systems
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with programming (Python, MATLAB), background in aerospace, computer science, robotics, or electrical engineering graduates, hands on skills in implementation of fusion/learning based techniques in
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software such as Matlab, R, Python or similar would be beneficial. The direction of the PhD is flexible and can be partially moulded to suit the strengths and interests of the successful candidate
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•Background with relevant packages, (CREO/SOLIDWORKS, ANSYS/ABAQUS, MATLAB) •A driven, professional and self-dependent work attitude is essential •Experience of working within the industry will be an advantage
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, mathematics or related field. Experience with computer programming languages (e.g., MatLab, Phyton), familiarity with geographic information systems and systems thinking methodologies and an interest in
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in applied mathematics, or a closely related subject with substantial mathematical content. Skills in programming (MATLAB, Julia) and numerical bifurcation theory (e.g. DDE-BIFTOOL, MATCONT
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computational elements, and some coding experience in a programming language (e.g., Python, MATLAB, Julia) are essential. The successful candidate will be supervised by: Dr Michael Short (https://www.surrey.ac.uk