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Veterinärmedizinische Universität Wien (University of Veterinary Medicine Vienna) | Austria | 14 days ago
Specific Requirements master's degree in computer science or a related field; programming skills in R and Python; experience with mathematical modelling of animal data; experience with mathematical
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-structure-sediment interactions Post-processing and analysis of large datasets using Python/MATLAB Supervision of project-related student internships and master theses Dissemination of results through
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'Bioinformatics Algorithms', 2013, available at: https://www.uni-ulm.de/in/theo/m/ohlebusch/ or the book 'Genome-Scale Algorithm Design' by Makinen et al, 2nd edition https://www.genome-scale.info
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staff to carry out activities related to research lines or scientific-technical services Ref. Interna F1030-7812 PID2022-139551NB-I00 Call for selection of research asistants https://sede.urjc.es
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(e.g., Python, MATLAB), statistical programming languages (e.g., R, SPSS, SAS), and/or stimulus presentation software (e.g., Psychtoolbox, Presentation, ExperimentBuilder, PsychoPy) is desired. 5
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) and a global ocean model (NEMO). The selected candidate will contribute to the ANR-AIAI project (https://anr-aiai.github.io ). Scientific Context The melting of Antarctic ice shelves by the ocean is a
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. Experience using Python in a professional setting. Experience using data visualization tools (i.e. Tableau, Power BI or similar) Highly proficient in MS Office software with advanced knowledge of Excel. Strong
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physics. Experience in surface science, crystallography, Python programming, and/or quantum chemistry will be considered an asset. Excellent communication skills are essential, with the ability to work and
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skills: Strong background in chemistry, physical chemistry, materials science, or condensed matter physics. Experience in data science, Python programming, high-performance computing and/or quantum
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mechanics) with data-driven techniques. • Proficiency in Python (NumPy, TensorFlow/PyTorch) and MATLAB. • Familiarity with deep learning architectures, particularly recurrent neural networks (e.g., LSTM) and