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- Tasks: Own calibration, inversion, and uncertainty quantification pipelines using advanced numerical methods.
- Company: Bespoke Labs, a forward-thinking company in the UK.
- Benefits: Competitive compensation, fully asynchronous work, and a commitment to inclusivity.
- Other info: Join a dynamic team focused on innovation and inclusivity.
- Why this job: Make a real impact in cutting-edge physics-informed machine learning projects.
- Qualifications: PhD or active PhD candidate in a relevant field with Python expertise.
The predicted salary is between 20000 - 30000 £ per year.
Bespoke Labs in the United Kingdom is looking for a contractor to take ownership of calibration, inversion, and uncertainty quantification pipelines.
Candidates should hold a PhD or be an active PhD candidate in a relevant discipline.
Essential skills include:
- Expertise in numerical methods
- Proficiency in Python with scientific libraries
- The ability to implement algorithms from primary literature
This fully asynchronous role offers competitive compensation and a commitment to inclusivity.
Physics‑Informed ML Engineer (PhD Intern, Contract) employer: Bespoke Labs
Contact Detail:
Bespoke Labs Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Physics‑Informed ML Engineer (PhD Intern, Contract)
✨Tip Number 1
Network like a pro! Reach out to your connections in the field of physics and machine learning. Attend relevant meetups or webinars, and don’t be shy about asking for introductions to people at Bespoke Labs.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects related to numerical methods and Python. This will give you an edge when discussing your experience during interviews.
✨Tip Number 3
Prepare for technical interviews by brushing up on algorithms from primary literature. Be ready to discuss how you've implemented these in your past work or research.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who take the initiative to engage directly with us.
We think you need these skills to ace Physics‑Informed ML Engineer (PhD Intern, Contract)
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your expertise in numerical methods and Python. We want to see how you can implement algorithms from primary literature, so don’t hold back on showcasing your relevant projects or experiences!
Tailor Your Application: Take a moment to customise your application for this role. Mention how your PhD work aligns with the responsibilities of calibration, inversion, and uncertainty quantification pipelines. We love seeing candidates who connect their background to what we do!
Be Clear and Concise: When writing your application, keep it clear and to the point. We appreciate well-structured applications that are easy to read. Use bullet points if necessary to make your skills and experiences stand out!
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it gives you a chance to explore more about what we do at StudySmarter!
How to prepare for a job interview at Bespoke Labs
✨Know Your Stuff
Make sure you brush up on your numerical methods and Python skills. Be ready to discuss specific algorithms you've implemented from primary literature, as this will show your depth of knowledge and practical experience.
✨Showcase Your Projects
Prepare to talk about any relevant projects or research you've done during your PhD. Highlight how you've tackled calibration, inversion, or uncertainty quantification in your work, as this will demonstrate your hands-on experience in the field.
✨Ask Insightful Questions
Think of some thoughtful questions to ask about the role and the team at Bespoke Labs. This shows your genuine interest in the position and helps you understand if it's the right fit for you.
✨Be Yourself
Remember that this is a conversation, not an interrogation. Be authentic and let your passion for physics-informed machine learning shine through. The interviewers want to see who you are beyond your CV!