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limited to a record of scholarly publications Experience in programming and code development (python, R) Proven ability to publish manuscripts in peer-reviewed scientific journals Fluent English, both
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Professional Experience Experience with UNIX/LINUX environments and scripting languages (bash) Basic knowledge of python Additional Knowledge and Professional Experience Knowledge of climate data formats (GRIB
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FUNDACIÓN PARA LA INVESTIGACIÓN BIOMÉDICA DEL HOSPITAL UNIVERSITARIO PUERTA DE HIERRO | Spain | about 1 month 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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Professional Experience Strong programming skills in Python and familiarity with machine learning libraries such as PyTorch, TensorFlow, or Scikit-learn. Experience in handling, analyzing, and validating large
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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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., Python, C++) Familiarity with ML algorithms, especially deep learning Experience in R&D or industrial projects carried out either in the context of research institutions or industry Testbed management and
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of technological tools. - Process and measurement automation with LabView or similar. - Tasks related to artificial intelligence and machine learning applications in health, wellness and sport. - Creation of Python
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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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candidate should possess a knowledge of both time-series and frequency-domain analysis, as well as proficiency in Python or Matlab. Responsibilities include detecting cells and cellular organelles in image
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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