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Risk Analysis Group at TUM and Prof. Maria Pina Limongelli at Politecnico di Milano, offering an excellent international research environment. About us The Engineering Risk Analysis Group at TUM
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of working in a multidisciplinary, international consortium?Are you familiar with Python, MATLAB, or similar tools for data analysis and optimization?Are you eager to contribute to EU-wide goals on energy
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English. Solid programming skills in Matlab, Python, or C/C++. Great emphasis will be placed on personal qualities such as strong collaboration and communication skills with other researchers and project
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strong synergy with boosted analyses we are performing in other two-Higgs channels. This position is co-supervised by prof. dr. Ivo van Vulpen, dr. Clara Nellist, and dr. Sascha Caron. The candidates is
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group of Prof. Felix Deschler under the remit of the collaborative research center SFB 1249. The positions are available immediately (fixed term contract, end of funding period 31.12.2028). These 3-year
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mechanics in general and the following skills in particular: continuum mechanics for large deformations numerical mechanics and analysis (FEM simulations, meshing, etc.) programming skills (Matlab/Python
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normative modelling is an advantage. Programming skills (Python, C/C++) and experience with MRI morphometry are desirable. Furthermore, the applicant should be able to work both independently and in a team
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) Research experience in neuroscience, biological psychiatry, or mental health Skills in statistical analysis and programming (e.g., R, Python); willingness to develop these skills is essential Strong ability
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psychoneuroendocrinology, enthusiasm for interdisciplinary research, and strong skills in experimental set-ups/performing lab-based studies and statistics. Programming skills (Python, R, Matlab) and experience with MRI data
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. Essential qualifications include solid programming skills (preferably in Python and R), proven experience in the analysis of large-scale datasets (e.g., NGS, metabolomics), and familiarity with statistical