Post-Doctoral Research Associate (Fixed Term)

University of Cambridge; Cambridge; On-site; GBP 30,000 - 40,000; Full time

Job summary

The University of Cambridge is looking for a highly motivated Post-Doctoral Research Associate to lead the ERC PoC project PepVerse. This unique role involves pioneering research in peptide-based drug discovery, requiring a PhD in a relevant field and strong analytical skills. Candidates should have hands-on experience in protein sciences and the ability to collaborate in multidisciplinary teams while utilizing state-of-the-art AI assistance.

Qualifications

• PhD holders preferred, additional industry or post-doctoral experience is a plus.
• Hands-on experience with experimental technology in protein sciences.
• Publications demonstrating robust problem-solving skills.

Responsibilities

• Lead research on ERC PoC project PepVerse.
• Drive commercialization of a peptide-based drug discovery platform.
• Develop and leverage AI technologies to streamline research processes.

Skills

Analytical skills

Protein sciences

AI technologies

Quantitative analysis

Education

PhD in biochemistry, molecular biology, or related discipline

Tools

mRNA display-based assays

Peptide libraries

Job description

Post-Doctoral Research Associate (Fixed Term)

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Post-Doctoral Research Associate (Fixed Term)

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Fixed-term: The funds for this post are available for 18 months in the first instance.

We are looking for a highly motivated Postdoctoral Research Associate (PDRA) with interest in commercialising scientific breakthroughs. The successful candidate will lead research on ERC PoC project PepVerse within Prof. Knowles' research group at the Yusuf Hamied Department of Chemistry at the University of Cambridge. This is a unique opportunity to drive the commercialisation of a cutting-edge platform that accelerates peptide-based drug discovery.

Peptide-based drugs present significant advantages over existing molecular therapeutics. Their ability to target challenging biological sites and modulate intracellular functions¿capabilities beyond those of traditional small molecules and biologics¿make them a promising class of medicines. However, progress in this area has been limited by the absence of high-quality, large-scale protein¿peptide interaction datasets critical for rational, AI-driven peptide design. To address this challenge, as part of ERC Consolidator grant, Prof. Knowles' research group developed a platform that enables high-throughput screening of peptide-based libraries against protein targets. The platform can be used to identify peptide-based binders across a variety of protein targets and provide starting points for drug discovery programmes. Moreover, it has the potential to create the world's largest protein/peptide mimotope dataset foundational for advancing any peptide-based drug discovery programme. The ERC PoC PepVerse project focuses on increasing the technology readiness of this platform, transitioning it from a promising research tool to a commercially attractive platform.

Prof. Knowles' research group uses experimental and computational biophysics and physical chemistry to study human health and disease with a particular focus on proteins and peptides. Prof. Knowles has founded and spun out five companies from his lab's research, addressing critical challenges in biotechnology, drug development, and materials science.

The successful candidate will have: - PhD in biochemistry, molecular biology, protein sciences or a related discipline. Additional research experience in industry or a post-doctoral setting is a plus. - Hands-on experience with a broad range of experimental technology in protein sciences and in working with peptides - Track record of publications that reflect professional expertise and robust problem solving skills - Excellent written and oral communication abilities, with a proven capacity to collaborate effectively within multidisciplinary teams and with industry partners - Proven ability to efficiently plan and execute multifaceted research projects with multiple inter-dependent components - Demonstrated ability to effectively leverage AI assistive technologies, such as ChatGTP and Claude, to streamline processes - Hands-on experience in developing mRNA display-based assays or in designing or utilising peptide libraries incorporating non-natural amino acids, is highly desirable. - Strong analytical skills, proficiency in quantitative analysis of large datasets and working with pre-trained machine learning models is desirable but not essential

Click the 'Apply' button below to register an account with our recruitment system (if you have not already) and apply online.

Please ensure that you upload your Curriculum Vitae (CV), a covering letter and publications list in the upload section of the online application. If you upload any additional documents that have not been requested, we will not be able to consider these as part of your application.

For queries relating to the position or the application process, please contact Dr Kadi Saar on (email: [e-mail]).

Please quote reference MA45283 on your application and in any correspondence about this vacancy.

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.

The Department holds an Athena SWAN silver award for women in Science, Technology, Engineering, Mathematics, and Medicine.

The University has a responsibility to ensure that all employees are eligible to live and work in the UK.

Further information

• Further information
  
  

Apply online

Seniority level

• Seniority level

  Internship

Employment type

• Employment type

  Full-time

Job function

• Job function

  Research, Analyst, and Information Technology

• Industries

  Research Services

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