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Instituto de Investigación Sanitaria Puerta de Hierro – Segovia de Arana (IDIPHISA) | Spain | 16 days ago
techniques to optimize the use of language models and artificial intelligence o Advanced use of Python for data analysis, process automation, and solution development o Statistical analysis o Cell
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Unix/Linux operating systems Strong programming skills in Python and/or R, as well as Bash Additional information: We offer: 📈The possibility of developing your professional career in a competitive
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-resolved transcriptomics, or similar). Programming experience (e.g., R, Python, MATLAB) and familiarity with relevant bioinformatics pipelines. Interest in neuroscience, developmental biology, and evolution
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Omics data analysis (mainly single-cell) using bioinformatics and computational biology methods with R and Python. Specific Requirements 1. Be a Spanish national, a national of a European Union member
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(extract, transform and load). Experience in data cleaning. Experience in python and R programming. Experience with relational and non-relational databases. Experience and knowledge of the Linux environment
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Method (FEM) (8 points). Educational background and proven experience in modelling mathematical programming problems using programming languages such as Python, C, Fortran, etc. (8 points). Proven
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FUNDACIÓN PARA LA INVESTIGACIÓN BIOMÉDICA DEL HOSPITAL UNIVERSITARIO PUERTA DE HIERRO | Spain | 4 days ago
knowledge of cybersecurity. Other merits to be assessed: • Knowledge of programming languages (Java, SQL, PrimeFaces, Python, PHP, HTML) and databases (SQL, MySQL, MariaDB). • Experience in using
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. Requirements for candidates: Essential: BSc (completed or near completion) in Computational Biology, Biochemistry, Biology, or a related quantitative field. Demonstrated proficiency in R and/or Python for data
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-processing environments (MATLAB, Python). 4. Familiarity with MIMO and beamforming concepts, ideally at mm-wave or THz frequencies. 5. Hands-on experience or strong interest in hardware prototyping and
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the job profile (sections 2 and 3 of this annex), as well as to: Programming in Python Use of machine learning libraries: TensorFlow, PyTorch and scikit-learn Development of supervised and unsupervised