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- Tasks: Develop advanced machine learning functions for real-time detection of cassava viral infections.
- Company: Join a multidisciplinary team from top universities and research institutes worldwide.
- Benefits: Enjoy a pension scheme, health services, generous leave, and discounts at retailers.
- Other info: Opportunities for professional development and travel within a dynamic project environment.
- Why this job: Make a real-world impact on agriculture with cutting-edge technology and international collaboration.
- Qualifications: Experience in machine learning, programming skills, and ability to work in diverse teams.
The predicted salary is between 40000 - 55000 £ per year.
Overview
The overall role is to develop advanced multispectral image processing and machine learning functions on a real-time embedded system for detecting cassava viral infection from scanned leaves in the field. This role is part of a multidisciplinary, international project between Rutgers University (USA), North Carolina State University (USA), International Institute of Tropical Agriculture (IITA) (Tanzania), Rothamsted Research (UK), and the University of Manchester (UK). The role contributes to the development of machine learning algorithms on in-house built low-cost multispectral imaging systems for detecting cassava brown streak virus in the field. The project aims to expand the technology to in-situ detection, characterization and monitoring of cassava growth and quality control, funded through the NSF and BBSRC Joint Programme (EEID). There are professional development and travel budgets and opportunities.
Responsibilities
- Develop advanced multispectral image processing and machine learning functions on real-time embedded systems.
- Collaborate with international project partners to integrate algorithms with hardware platforms.
- Validate and optimize detection of cassava brown streak virus in field conditions.
- Contribute to field deployment, data collection, and ongoing system improvements.
Qualifications
- Experience in multispectral imaging, computer vision, and machine learning for embedded or real-time systems.
- Strong programming skills (e.g., Python, C/C++, MATLAB) and experience with ML frameworks.
- Ability to work in a multidisciplinary, international team and in field conditions.
Benefits
- Pension scheme
- Employee health and wellbeing services including an Employee Assistance Programme
- Starting annual leave entitlement plus bank holidays
- Additional paid closure over the Christmas period
- Local and national discounts at retailers
Equal Opportunity
As an equal opportunities employer we welcome applicants from all sections of the community regardless of age, sex, gender (or gender identity), ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit. Hybrid working arrangements may be considered.
Machine Learning Specialist in Manchester employer: The University of Manchester
Contact Detail:
The University of Manchester Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Specialist in Manchester
✨Tip Number 1
Network like a pro! Reach out to your connections in the machine learning and imaging fields. Attend relevant meetups or webinars, and don’t be shy about asking for introductions to people working on similar projects.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your work with multispectral imaging and machine learning. Include any projects that demonstrate your ability to develop algorithms or work with embedded systems.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge. Be ready to discuss your experience with Python, C/C++, and MATLAB, and how you’ve applied these in real-time systems. Practice explaining complex concepts in simple terms.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are proactive and engaged with our mission.
We think you need these skills to ace Machine Learning Specialist in Manchester
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in multispectral imaging and machine learning. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects or achievements!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about this project and how your background makes you a perfect fit. We love seeing enthusiasm and a personal touch!
Showcase Your Technical Skills: Since this role involves programming and real-time systems, make sure to mention your proficiency in languages like Python, C/C++, or MATLAB. We’re keen to know how you’ve applied these skills in past projects!
Apply Through Our Website: Don’t forget to submit your application through our website! It’s the best way for us to receive your materials and ensures you’re considered for the role. We can’t wait to hear from you!
How to prepare for a job interview at The University of Manchester
✨Know Your Tech
Make sure you brush up on your knowledge of multispectral imaging and machine learning algorithms. Be ready to discuss specific projects you've worked on, especially those involving real-time embedded systems. This will show that you not only understand the theory but also have practical experience.
✨Collaborate Like a Pro
Since this role involves working with international partners, be prepared to talk about your experience in multidisciplinary teams. Share examples of how you've successfully collaborated with others, especially in challenging environments or field conditions.
✨Showcase Your Coding Skills
You’ll need strong programming skills, so be ready to demonstrate your proficiency in Python, C/C++, or MATLAB. Consider preparing a small coding challenge or discussing a piece of code you've written that relates to machine learning or image processing.
✨Ask Insightful Questions
Prepare thoughtful questions about the project and its goals. Inquire about the challenges they face in detecting cassava viral infections and how your role can contribute to overcoming them. This shows your genuine interest and helps you understand the position better.