Research Assistant/Associate in Data Science for Construction Productivity (Fixed Term)

A position exists for a Research Assistant/Associate in the Department of Engineering to work on Data Science for Construction Productivity. The researcher's responsibilities include developing and implementing machine learning (ML), computer vision (CV), large language models (LLMs), and vision-language models (VLMs) to automate data extraction and interpretation for productivity measurement in construction.

The required skills, qualifications, and experience are:

• Hold (or be close to obtaining) a PhD in Computer Science, Civil Engineering, Data Science, Information Systems, or a related field.
• Strong analytical and critical thinking skills.
• Strong ML, CV, and LLM skills for quantitative data, texts, images, and sensor-based data.
• Experience in automated collection of unstructured and structured data in different formats, and translating these into a standard format for analysis, interpretation, and dashboard development.
• Experience working with industry partners is desirable.
• Excellent English language proficiency.
• Excellent verbal and non-verbal communication skills, including writing concise and well-presented text in academic papers and/or industry reports.
• Evidence of working collaboratively in multidisciplinary teams and liaising with stakeholders including academics and industry.

The Laing O'Rourke Centre supports flexible work arrangements. Core working time at the Centre based in the West Cambridge Civil Engineering offices is expected.

Salary ranges: Research Assistant: £33,002 - £35,608; Research Associate: £37,694 - £46,049. Fixed-term: The funds for this post are available for 12 months in the first instance.

To apply, click the 'Apply' button to register an account and apply online. Upload your CV, covering letter, degree certificates, transcript, and research publication list. Submit by midnight on the closing date.

For questions, contact Jan Wojtecki at jw463@cam.ac.uk for application process queries, or Dr Brian Sheil at bbs24@cam.ac.uk for technical queries. Quote reference NM47014.

The University actively supports equality, diversity, and inclusion and encourages applications from all sections of society. The University ensures all employees are eligible to live and work in the UK.