At a Glance
- Tasks: Develop AI and machine learning systems for groundbreaking research in synthetic biology.
- Company: Join the innovative Ellison Institute of Technology, tackling global challenges with cutting-edge science.
- Benefits: Competitive salary, travel allowance, enhanced holiday pay, and private medical insurance.
- Why this job: Make a real-world impact in health and sustainability while working with visionary scientists.
- Qualifications: PhD in relevant field and experience in AI/ML for biological design tasks.
- Other info: Dynamic team environment with opportunities for professional growth and collaboration.
The predicted salary is between 36000 - 60000 £ per year.
At the Ellison Institute of Technology (EIT), we’re on a mission to translate scientific discovery into real world impact. We bring together visionary scientists, technologists, policy makers, and entrepreneurs to tackle humanity’s greatest challenges in four transformative areas:
- Health, Medical Science & Generative Biology
- Food Security & Sustainable Agriculture
- Climate Change & Managing COâ‚‚
- Artificial Intelligence & Robotics
This is ambitious work - work that demands curiosity, courage, and a relentless drive to make a difference. At EIT, you’ll join a community built on excellence, innovation, tenacity, trust, and collaboration, where bold ideas become real-world breakthroughs. Explore more at www.eit.org.
Welcome to the Generative Biology Institute: The Generative Biology Institute (GBI) at the Ellison Institute of Technology (EIT) aims to overcome two major challenges in making biology engineerable:
- the ability to precisely synthesize entire genomes, and
- understanding which DNA sequences will create biological systems that perform desired functions.
Solving these challenges will unlock the potential of biology for transformative solutions in health, sustainability, agriculture, and more. GBI will house 30 groups and over 600 researchers, supported by cutting-edge facilities and sustained funding to address global challenges and advance biology engineering.
Your Role: At EIT we are seeking an experienced and detailed orientated ML Scientist (Senior & Non Senior) to develop AI and machine learning systems that drive and catalyze GBI’s scientific aims, working alongside researchers and platform staff to address key questions in biological sequence design and discovery. The post-holder will work with multiple data modalities with a focus on sequence-to-function modelling, prediction and optimisation. This is an exceptional opportunity to join a new unit at the forefront of AI/ML and synthetic biology with access to exceptional facilities and expertise. We are looking for colleagues who thrive in a team and care deeply about biological questions, hypotheses, and a biology-centric approach to AI/ML engineering. The role requires broad technical expertise in applied machine learning and prior exposure to synthetic biology design tasks in collaboration with wet lab scientists. Our team ethos is based on mutual learning, strong peer-to-peer support, and a deep sense of scientific curiosity and ambition. We are hiring for two roles at regular or senior level depending on experience.
Key Responsibilities:
- Design and build AI and machine learning systems to address GBI’s research challenges in synthetic biology, genome design, and molecular evolution.
- Lead and contribute to collaborative projects with GBI researchers, staff, and external collaborators.
- Work closely with GBI wet lab scientists in co-creation of research projects and development of fit-for-purpose computational solutions.
- Provide expert machine learning knowhow to GBI researchers and scope novel avenues of research.
- Interact with the Bioinformatics and Scientific Compute platforms to support the development of GBI data flows and MLOps.
- Ensure compliance with best practices in ML engineering, including robust and reproducible training pipelines, as well as versioning and documentation of data, models, and code.
- Keep abreast of progress in AIxBio and make use of strategic learning opportunities.
- Lead and contribute to research publications in prestigious venues.
- Organise and prioritise work, operating at the highest standard in the face of multiple competing deadlines.
- Promote and champion EIT and the work of the GBI, representing the institute at functions and public events.
Essential Knowledge, Skills and Experience:
- PhD degree in a suitable field including, but not limited to, mathematics, computer science, molecular biology, computational biology, engineering, or related discipline.
- Desirable: at least 2 years of industry or postdoctoral experience in similar roles.
- Experience in building AI or machine learning models for biological design tasks, involving processing, visualizing, and analysing various data modalities in collaboration with wet lab scientists and using a breadth of methods and architectures (such as classic statistical learning, genomic/protein foundation models, deep learning, geometric learning, representation learning, multi-modal learning, active learning).
- Ability to abstract high-level biological questions and translate them into actionable machine learning tasks, evidenced by previous achievements in a comparable industry role, or a promising publication record in scientific journals and technical conferences.
- Ability to learn quickly and dive into a range of problem spaces and computational methods.
- Ability to work and communicate with and within diverse and multidisciplinary teams.
- Fluency in one or more scientific programming languages (Python, R, Julia, etc) with experience in best practices in machine learning, including documentation.
- Excellent written and oral communication skills for diverse audiences, including colleagues without a computational background.
- Excellent time management skills across competing tasks requiring rapid context switching.
Our Benefits:
- Competitive Salary + travel allowance + bonus
- Enhanced holiday pay
- Pension
- Life Assurance
- Income Protection
- Private Medical Insurance
- Hospital Cash Plan
- Therapy Services
- Perk Box
- Electric Car Scheme
Working Together – What It Involves:
You must have the right to work permanently in the UK with a willingness to travel as necessary. In certain cases, we can consider sponsorship, and this will be assessed on a case-by-case basis. You will live in, or within easy commuting distance of, Oxford (or be willing to relocate).
ML Scientist (Senior & Non Senior) - Generative Biology Institute Generative Biology Institute [...] in Oxford employer: Ellison Institute, LLC
Contact Detail:
Ellison Institute, LLC Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land ML Scientist (Senior & Non Senior) - Generative Biology Institute Generative Biology Institute [...] in Oxford
✨Tip Number 1
Network like a pro! Reach out to current employees at the Generative Biology Institute through LinkedIn or other platforms. A friendly chat can give you insider info and might even lead to a referral.
✨Tip Number 2
Prepare for those interviews! Research the latest trends in AI and synthetic biology, and think about how your skills can contribute to their mission. Show them you’re not just a fit, but the perfect fit!
✨Tip Number 3
Don’t underestimate the power of follow-ups! After an interview, drop a thank-you email to express your appreciation and reiterate your enthusiasm for the role. It keeps you fresh in their minds.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in being part of the EIT community.
We think you need these skills to ace ML Scientist (Senior & Non Senior) - Generative Biology Institute Generative Biology Institute [...] in Oxford
Some tips for your application 🫡
Show Your Passion for Biology: When writing your application, let your enthusiasm for biology shine through! We want to see how your curiosity and drive align with our mission at the Generative Biology Institute. Share specific examples of how you've tackled biological questions in your past work.
Tailor Your Experience: Make sure to highlight your relevant experience in AI and machine learning, especially in relation to biological design tasks. We’re looking for candidates who can bridge the gap between computational methods and wet lab science, so be clear about your collaborative projects!
Be Clear and Concise: Keep your application straightforward and to the point. Use clear language to describe your skills and experiences, and avoid jargon unless it’s necessary. We appreciate well-structured applications that are easy to read and understand.
Apply Through Our Website: Don’t forget to submit your application through our website! It’s the best way for us to receive your details and ensures you’re considered for the role. Plus, it shows you’re keen on joining our community at EIT!
How to prepare for a job interview at Ellison Institute, LLC
✨Know Your Stuff
Make sure you brush up on your machine learning and synthetic biology knowledge. Be ready to discuss specific projects you've worked on, especially those involving AI models for biological design tasks. This will show that you can translate high-level biological questions into actionable ML tasks.
✨Show Your Team Spirit
Since collaboration is key at the Generative Biology Institute, be prepared to talk about your experience working in multidisciplinary teams. Share examples of how you've successfully collaborated with wet lab scientists or other researchers to achieve common goals.
✨Ask Smart Questions
Prepare insightful questions about the GBI's current research challenges and how your skills can contribute. This not only shows your genuine interest in the role but also demonstrates your proactive approach to problem-solving in a team setting.
✨Communicate Clearly
Practice explaining complex concepts in simple terms, as you'll need to communicate with colleagues who may not have a computational background. Clear communication is essential, so think about how you can convey your ideas effectively during the interview.