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Key Performance Areas

Deliver effective and high-quality teaching, learning experiences, and assessments; supervise student research projects; contribute to departmental administration and governance, actively participate in curriculum development and review processes; engage in research activities and develop a robust portfolio of publications; participate in relevant departmental, faculty, and university committees to support institutional goals; assist in the selection and registration of students, ensuring alignment with academic standards; provide capacity-building services to the community, fostering outreach and engagement and fulfil any additional reasonable tasks and responsibilities as assigned by superiors.

 Job Requirements

A Doctorate (NQF Level 10) in Highway Engineering/Transport Engineering, Civil Engineering, or related field with at least seven (7) years of lecturing experience at tertiary education level and/or industry experience, or an equivalent combination of relevant professional experience.  Excellent English communication skills (oral and written).  Proven competence in sourcing of research or project funding

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Synopsis

Machine Learning and Artificial Intelligence (AI) are enabling extraordinary scientific breakthroughs in fields ranging from protein folding, natural language processing, drug synthesis, and recommender systems to the discovery of novel engineering materials and products. These achievements lie at the confluence of mathematics, statistics, engineering and computer science, yet a clear explanation of the remarkable power and also the limitations of such AI systems has eluded scientists from all disciplines. Critical foundational gaps remain that, if not properly addressed, will soon limit advances in machine learning, curbing progress in artificial intelligence. It appears increasingly unlikely that these critical gaps can be surmounted with increased computational power and experimentation alone. Deeper mathematical understanding is essential to ensuring that AI can be harnessed to meet the future needs of society and enable broad scientific discovery, while forestalling the unintended consequences of a disruptive technology.  

The National

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RSS Feed Source: Academic Keys

Synopsis

The Mathematical Biology Program supports research in all areas of mathematical sciences with relevance to the biological sciences. Successful proposals must demonstrate mathematical innovation, biological relevance and significance, and strong integration between mathematics and biology.

Some projects of interest to the Mathematical Biology Program may include development of mathematical theories, methodologies, and tools traditionally seen in other disciplinary programs within the Division of Mathematical Sciences. In general, if a proposal is appropriate for review by more than one NSF program, it is advisable to contact the program officers handling each program to determine when and where the proposal should be submitted and to facilitate the review process.

The Mathematical Biology Program regularly seeks joint reviews of proposals with programs in the Directorates of Biological Sciences and other relevant programs. Investigators are encouraged to discuss their project with program officers in relevant areas to

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On July 10, 2025, NSF issued an Important Notice providing updates to the agency’s research security policies, including a research security training requirement, Malign Foreign Talent Recruitment Program annual certification requirement, prohibition on Confucius institutes and an updated FFDR reporting and submission timeline.

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