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- Tasks: Develop genome-scale models and design machine learning algorithms for sustainable biorefineries.
- Company: Join a leading UK university in a multidisciplinary research project.
- Benefits: Competitive salary, access to high-performance computing facilities, and career development opportunities.
- Why this job: Make a real impact on sustainability through cutting-edge research in computational biology.
- Qualifications: Experience in computational biology, machine learning, and data analysis required.
- Other info: Full-time, fixed-term role with excellent collaboration opportunities across top universities.
The predicted salary is between 35608 - 46049 £ per year.
We are seeking a highly motivated Postdoctoral Research Associate (PDRA) to join an exciting UKRI-funded project titled “AI-Driven Metabolic Modelling and Sustainability Assessment for Next-Generation Biorefineries.”
The project aims to accelerate the design of sustainable biorefineries by leveraging computational biology and artificial intelligence to optimise microbial strain engineering and bioprocess strategies for converting agro-industrial residues into high-value chemicals.
The successful candidate will:
- Develop genome-scale metabolic models (GEMs) for engineered microbial strains.
- Design and implement machine learning algorithms to predict strain performance and optimise metabolic pathways.
- Integrate multi-omics datasets into computational workflows for systems-level analysis.
- Perform techno-economic analysis (TEA) and life cycle assessment (LCA) using tools such as Aspen Plus, openLCA, and BioSTEAM.
You will work within a multidisciplinary consortium of three leading UK universities and two industrial partners, contributing to the UK’s transition toward a circular bioeconomy. The role offers access to Loughborough University’s High-Performance Computing (HPC) facilities, including the Lovelace cluster and regional Tier-2 resources, supporting large-scale computational modelling and machine learning tasks.
The position is full-time and fixed-term for 24 months. Salary will be on Specialist and Supporting Academic Grade 6, from £35,608 - £46,049 per annum.
Research Associate in Computational Biology and Machine Learning - Loughborough employer: Loughborough University
Contact Detail:
Loughborough University Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Research Associate in Computational Biology and Machine Learning - Loughborough
✨Tip Number 1
Network like a pro! Reach out to professionals in computational biology and machine learning on platforms like LinkedIn. Join relevant groups, attend webinars, and don’t hesitate to ask for informational interviews. We all know that sometimes it’s not just what you know, but who you know!
✨Tip Number 2
Prepare for those interviews! Research the company and the project thoroughly. Understand their goals around AI-driven metabolic modelling and sustainability. We recommend practising common interview questions and even some technical ones related to genome-scale metabolic models and machine learning algorithms.
✨Tip Number 3
Showcase your skills! Create a portfolio or GitHub repository with your projects related to computational biology and machine learning. This is a great way to demonstrate your expertise and passion. We love seeing practical examples of your work!
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we often have exclusive opportunities listed there. Don’t miss out on your chance to be part of this exciting project at Loughborough!
We think you need these skills to ace Research Associate in Computational Biology and Machine Learning - Loughborough
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the role of Research Associate in Computational Biology and Machine Learning. Highlight relevant experience, especially in computational biology, machine learning, and any projects that align with sustainable biorefineries.
Craft a Compelling Cover Letter: Your cover letter should tell us why you're the perfect fit for this role. Share your passion for AI-driven metabolic modelling and how your skills can contribute to the project’s goals. Be genuine and let your enthusiasm shine through!
Showcase Your Technical Skills: Don’t forget to mention your technical skills! If you’ve worked with genome-scale metabolic models or have experience with tools like Aspen Plus or openLCA, make sure to include that. We want to see how you can hit the ground running!
Apply Through Our Website: We encourage you to apply through our website for a smooth application process. It’s the best way to ensure your application gets to us directly and allows you to keep track of your application status easily.
How to prepare for a job interview at Loughborough University
✨Know Your Stuff
Make sure you brush up on your knowledge of computational biology and machine learning. Familiarise yourself with genome-scale metabolic models and the specific tools mentioned in the job description, like Aspen Plus and openLCA. Being able to discuss these topics confidently will show that you're genuinely interested and well-prepared.
✨Showcase Your Projects
Prepare to talk about any relevant projects you've worked on, especially those involving machine learning algorithms or multi-omics datasets. Highlight your role in these projects and the impact they had. This will help demonstrate your hands-on experience and problem-solving skills.
✨Ask Smart Questions
Come prepared with insightful questions about the project and the team you'll be working with. Inquire about the specific challenges they face in optimising microbial strain engineering or how they envision the integration of AI in their workflows. This shows your enthusiasm and critical thinking.
✨Be Yourself
While it's important to showcase your technical skills, don't forget to let your personality shine through. The interviewers are looking for someone who fits well within their multidisciplinary team. Be genuine, express your passion for sustainability and bioeconomy, and let them see the real you!
