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2D convolutional neural networks in Python. This is a part-time position (5 hours/week) funded until 31/03/2026 with a possibility of extension and is suitable for a Ph.D. student with relevant
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PhD Expert knowledge of statistical modelling, statistical inference and use of relevant software (e.g. R/Python) both for data processing, visualisation and programming. Excellent communication and
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years and in the relevant areas of Machine Learning / Artificial Intelligence, Credit Risk Modeling and Operations Optimization Modeling; The candidate must have strong programming skills in Python, and
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languages (Python/MATLAB) commonly used in machine learning applications, is desirable but learning can be completed during the PhD. Excellent communication and interpersonal skills to facilitate
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such as, programming languages (i.e. Python), knowledge of the handling of digital imaging and communications in medicine (DICOM) and some experience with picture archiving and communication systems (PACS
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undergraduate degree in a relevant computational domain, such as engineering, physics, neuroscience, computer science, applied mathematics. Skills in computer programming (especially Python or C++ or Matlab) and
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/computational biology/molecular biology/genomics or related area. The successful candidate will have considerable experience in computational bioinformatic analysis (R, Python or equivalent) of omics data and
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proficiency in Python (e.g., NumPy, Pandas, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with supervised
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/robotic systems Ability to implement control and kinematics with hardware-in-the–loop Background with relevant packages, (MATLAB, SolidWorks/Creo, ROS/ OpenCV/ python, LabVIEW/C languages) A driven
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to implement control and kinematics with hardware-in-the–loop ◾Background with relevant packages, (MATLAB, ROS/ OpenCV/ python, LabVIEW/C languages) ◾A driven, professional and self-dependent work attitude is