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- Tasks: Conduct innovative research in SLAM and large-scale graph optimisation.
- Company: Prestigious UK university with a vibrant academic environment.
- Benefits: Competitive salary, comprehensive benefits, and full-time position.
- Why this job: Join a leading research team and make significant contributions to robotics.
- Qualifications: PhD in Robotics, Computer Science or Mathematics; strong programming skills in C++ and/or Python.
- Other info: Collaborate with Dr Liang Zhao on cutting-edge research projects.
The predicted salary is between 36000 - 60000 £ per year.
A prestigious university in the UK is seeking two Postdoctoral Research Associates for funded positions in SLAM and large-scale graph optimisation. The roles require a PhD in Robotics, Computer Science or Mathematics, along with strong programming skills in C++ and/or Python. Successful candidates will contribute to innovative research alongside Dr Liang Zhao. This full-time position offers a competitive salary and comprehensive benefits in a vibrant academic environment.
Postdoc: SLAM & Large-Scale Graph Optimization employer: The University of Edinburgh
Contact Detail:
The University of Edinburgh Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Postdoc: SLAM & Large-Scale Graph Optimization
✨Tip Number 1
Network like a pro! Reach out to your academic contacts and let them know you're on the hunt for a postdoc position. They might have leads or even be able to put in a good word for you.
✨Tip Number 2
Show off your skills! Prepare a portfolio or a GitHub repository showcasing your projects in SLAM, graph optimisation, or any relevant programming work. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Practice makes perfect! Get ready for interviews by rehearsing common questions related to your field. We recommend doing mock interviews with friends or mentors to boost your confidence.
✨Tip Number 4
Apply through our website! We make it easy for you to find and apply for positions that match your skills. Don’t miss out on opportunities – keep an eye on our listings and submit your application!
We think you need these skills to ace Postdoc: SLAM & Large-Scale Graph Optimization
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your PhD and relevant experience in Robotics, Computer Science, or Mathematics. We want to see how your skills in C++ and Python shine through, so don’t hold back!
Craft a Compelling Cover Letter: Your cover letter is your chance to show us your passion for SLAM and large-scale graph optimisation. Share specific examples of your research and how it aligns with what Dr Liang Zhao is doing. Let your enthusiasm come through!
Showcase Your Programming Skills: Since strong programming skills are a must, include any projects or experiences that demonstrate your proficiency in C++ and/or Python. We love seeing practical applications of your coding skills!
Apply Through Our Website: To make sure your application gets the attention it deserves, apply directly through our website. It’s the best way for us to keep track of your application and ensure you’re considered for this exciting opportunity!
How to prepare for a job interview at The University of Edinburgh
✨Know Your Research
Make sure you’re well-versed in the latest advancements in SLAM and large-scale graph optimisation. Familiarise yourself with Dr Liang Zhao's work and be ready to discuss how your research aligns with theirs.
✨Show Off Your Coding Skills
Since strong programming skills in C++ and/or Python are crucial, prepare to demonstrate your coding abilities. You might be asked to solve a problem on the spot, so practice coding challenges related to robotics or algorithms.
✨Prepare Thoughtful Questions
Interviews are a two-way street! Prepare insightful questions about the research environment, team dynamics, and future projects. This shows your genuine interest in the role and helps you assess if it’s the right fit for you.
✨Practice Your Presentation
You may need to present your previous research or projects. Keep it concise and engaging, focusing on your contributions and the impact of your work. Practising in front of peers can help you refine your delivery.
