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considered. Experience of using machine learning algorithms and toolsets, ideally in a research context. Strong programming skills (e.g., Python, Java, C++). An interest in physiological signals. Home Student
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business datasets (e.g. ORBIS, Foreign Direct investment Data - UNCTAD) and appropriate experience with statistical software (e.g. Stata, R, Python). They will have a good understanding of, and interest in
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in computer vision and intelligent transportation. Experience with tools such as MATLAB, Python or machine learning frameworks is highly desirable. Supervisor: Dr Ning Zhao (N.Zhao@bham.ac.uk
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programming skills (ideally C/C++ and Python/MATLAB or similar), good communication skills, the ability to work independently and a willingness to engage with industrial partners. To apply, please contact
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assistantship. The ideal candidate will have a strong understanding of NLP, machine learning, and AI ethics, with proven skills in Python and frameworks like TensorFlow or PyTorch. Preference may be given
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languages such as Python, and experience of Artificial Intelligence and machine learning. They will also be familiar with working with real world data and analysing data based on large and diverse datasets
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the development of good programming skills (Python/MATLAB or similar), good communication skills, the ability to work independently, and a willingness to engage with industrial partners. To apply, please contact
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strategies. Proficiency in statistical programming languages (R, Python, or Stata) with verifiable experience in panel data analysis. Good communication abilities for effective knowledge dissemination
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or willingness to learn programming languages (e.g., Python, R) for handling and analysing clinical data. Additional Requirements: Ability to work independently and collaboratively within an interdisciplinary team
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: Experience with scripting languages such as Python for data analysis and machine learning applications and software development Data management skills: Proficiency in database management systems (DBMS