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- Tasks: Conduct research in network science and explore complex networks with expert guidance.
- Company: Join the prestigious University of Cambridge and be part of a leading research group.
- Benefits: Competitive fellowships, access to world-class resources, and a vibrant academic community.
- Other info: Collaborative environment with opportunities for personal and professional growth.
- Why this job: Shape the future of network science while pursuing your passion for research.
- Qualifications: Undergraduate or master's degree in a quantitative subject required.
The predicted salary is between 18000 - 25000 £ per year.
The Probabilistic Systems, Information, and Inference group at the University of Cambridge is seeking PhD students. Fellowships will be awarded on a competitive basis.
Among other things, the group studies random graphs, stochastic processes, and inference problems on complex networks. The thesis topic will be agreed to fit with student interest.
Applicants should have an undergraduate or masters degree in a quantitative subject.
Informal enquiries can be addressed to Dr George Cantwell, gtc31@cam.ac.uk.
Applications must be submitted at:
PhD positions in network science at University of Cambridge employer: Network Science Society, Inc.
Contact Detail:
Network Science Society, Inc. Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land PhD positions in network science at University of Cambridge
✨Tip Number 1
Network, network, network! Reach out to current PhD students or faculty members in the Probabilistic Systems group. A friendly chat can give you insights and might even lead to a recommendation.
✨Tip Number 2
Prepare for your interview like it’s the final exam! Brush up on your knowledge of random graphs and stochastic processes. We want to see your passion and understanding of the field.
✨Tip Number 3
Show us your unique angle! When discussing your thesis topic, highlight how your interests align with the group's research. Make it personal and relevant to what they’re doing at Cambridge.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets the attention it deserves. Plus, we love seeing candidates who follow the process closely.
We think you need these skills to ace PhD positions in network science at University of Cambridge
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your application to highlight your relevant skills and experiences. We want to see how your background in quantitative subjects aligns with the exciting work we're doing in network science!
Show Your Passion: Let us know why you're interested in this PhD position! Share your enthusiasm for random graphs and stochastic processes, and how you envision contributing to our research at Cambridge.
Be Clear and Concise: When writing your application, keep it straightforward and to the point. We appreciate clarity, so make sure your ideas flow well and are easy to understand.
Apply Through Our Website: Don't forget to submit your application through our official website! It’s the best way to ensure we receive all your materials and can review them promptly.
How to prepare for a job interview at Network Science Society, Inc.
✨Know Your Stuff
Make sure you brush up on your knowledge of network science, especially random graphs and stochastic processes. Familiarise yourself with recent research in the field, as this will show your passion and commitment to the subject.
✨Tailor Your Thesis Ideas
Think about potential thesis topics that align with both your interests and the group's research focus. Be ready to discuss these ideas during the interview, as it demonstrates your initiative and how you can contribute to their work.
✨Engage with the Interviewer
Don’t just wait for questions; engage Dr George Cantwell in a conversation. Ask insightful questions about the group’s current projects or future directions. This shows you’re genuinely interested and helps build rapport.
✨Showcase Your Quantitative Skills
Since a background in a quantitative subject is essential, be prepared to discuss your relevant coursework or projects. Highlight any specific skills or tools you've used, like programming languages or statistical software, that are applicable to network science.