At a Glance
- Tasks: Dive deep into machine learning research and develop innovative algorithms.
- Company: Join the University of Southampton, a leader in research and inclusivity.
- Benefits: Receive high-quality training, funding opportunities, and a flexible research environment.
- Other info: Applications considered on a rolling basis; apply early for the best chance!
- Why this job: Make a real impact in the exciting field of theoretical machine learning.
- Qualifications: Strong mathematical and programming skills required; MSc in relevant fields preferred.
The predicted salary is between 18000 - 25000 £ per year.
We are seeking highly qualified candidates for doctoral positions on the research of Machine Learning (ML). We are looking for a MSc in applied mathematics, computer science, operations research, statistics, engineering, mathematics, or a related discipline with strong theoretical training. The project focuses on the theory, algorithms and applications for ML based on generalised nonconvex low-rank models. The goals include designing faster algorithms, understanding the working principle behind ML, and applying ML to solve application problems.
This research is a mathematics-heavy project; it is not just about applying method X on data Y. You are expected to learn, understand, and develop theory to prove why method X will work, when method X will work, and how fast method X will converge. You will have the flexibility to choose a topic within the range of this project.
Area/Topics:
- Nonsmooth Nonconvex Optimisation
- Optimisation on manifold
- Analysis of optimisation algorithm by differential equations
- Submodularity and Combinatorial Optimisation
- Graph-theoretic machine learning
- Numerical Analysis / Numerical Linear Algebra
- Tensor-based signal processing
- Optimal transport for machine learning
- Foundation of machine learning via Clifford-Grassmann algebra
- Foundation of machine learning via Subtropical algebra
What we offer:
- High quality training to do theoretical machine learning
- Exciting theoretical research topics
- Flexible research environment
Requirement:
- Good mathematical skills
- Good programming skills in a numerical language (such as MATLAB, Python, or Julia)
Desirable skills:
- Good communication skills, both written and oral, in English
Entry Requirements:
- A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent).
- Evidence of good mathematical skills
Closing date: Applications will be considered in the order that they are received, and the position will be considered filled when a suitable candidate has been identified.
Funding: We offer a range of funding opportunities for both UK and international students, including Bursaries and Scholarships. Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered.
How To Apply:
- Apply online: Search for a Postgraduate Programme of Study (soton.ac.uk). Select programme type (Research), 2024/25, Faculty of Engineering and Physical Sciences, next page select “PhD Computer Science (Full time)”. In Section 2 of the application form you should insert the name of the supervisor Dr Andersen Ang.
- Applications should include:
- Research Proposal
- Curriculum Vitae
- Two reference letters
- Degree Transcripts/Certificates to date
For further information please contact: feps-pgr-apply@soton.ac.uk
The University of Southampton is committed to promoting equality, diversity, and inclusivity as demonstrated by our Athena SWAN award. We welcome all applicants regardless of their gender, ethnicity, disability, sexual orientation or age, and will give full consideration to applicants seeking to study part time. The University of Southampton takes personal circumstances into account, has onsite childcare facilities, is committed to sustainability and has been awarded the Platinum EcoAward.
PhD Studentship: Next Generation Machine Learning for Data Analysis in Southampton employer: University of Southampton
The University of Southampton is an exceptional employer for those pursuing a PhD in Machine Learning, offering a vibrant research environment that fosters innovation and collaboration. With a strong commitment to equality, diversity, and inclusivity, the university provides ample funding opportunities and support for both UK and international students, ensuring a rewarding academic journey. Additionally, the university's focus on sustainability and its Platinum EcoAward highlight its dedication to creating a positive impact both within and beyond the academic community.
StudySmarter Expert Advice🤫
We think this is how you could land PhD Studentship: Next Generation Machine Learning for Data Analysis in Southampton
✨Tip Number 1
Network like a pro! Reach out to current PhD students or faculty members in your field. They can provide insights about the programme and might even give you a heads-up on opportunities before they’re advertised.
✨Tip Number 2
Prepare for interviews by brushing up on your theoretical knowledge and programming skills. Be ready to discuss how your background aligns with the research focus, especially around nonconvex optimisation and machine learning applications.
✨Tip Number 3
Showcase your passion for the subject! When discussing your research interests, make sure to highlight how they connect to the project’s goals. This will demonstrate your commitment and understanding of the field.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application is seen. Plus, early applications have a better chance of securing funding, so get those materials ready and hit submit!
We think you need these skills to ace PhD Studentship: Next Generation Machine Learning for Data Analysis in Southampton
Some tips for your application 🫡
Tailor Your Research Proposal:Make sure your research proposal is spot on! We want to see how your ideas align with our project goals. Highlight your understanding of machine learning theories and how you plan to tackle the challenges in this area.
Show Off Your Skills:Don’t hold back on showcasing your mathematical and programming skills! Whether it’s MATLAB, Python, or Julia, let us know how you’ve used these tools in your previous work or studies. This is your chance to shine!
Craft a Stellar CV:Your CV should be more than just a list of qualifications. We’re looking for a narrative that connects your academic journey to this PhD opportunity. Include relevant projects, publications, or experiences that demonstrate your passion for machine learning.
Get Those References Ready:Choose your referees wisely! We want to hear from people who can vouch for your skills and potential in research. A strong reference can make all the difference, so give them a heads-up about what we’re looking for.
How to prepare for a job interview at University of Southampton
✨Know Your Maths Inside Out
Since this PhD is heavily focused on mathematics, make sure you brush up on your theoretical knowledge. Be prepared to discuss concepts like nonsmooth nonconvex optimisation and how they apply to machine learning. Practising problems and explaining your thought process can really impress the interviewers.
✨Show Off Your Programming Skills
You’ll need strong programming skills in languages like MATLAB, Python, or Julia. Before the interview, work on a small project or two that showcases your coding abilities. Be ready to talk about your experience with numerical analysis and how you've applied programming to solve complex problems.
✨Prepare Thoughtful Questions
Interviews are a two-way street! Prepare insightful questions about the research topics and the flexibility of the project. This shows your genuine interest and helps you gauge if the environment is right for you. Ask about the specific algorithms or theories you might explore during your PhD.
✨Communicate Clearly and Confidently
Good communication skills are essential, especially when discussing complex mathematical concepts. Practice explaining your ideas clearly and concisely. You might even want to do a mock interview with a friend to get comfortable articulating your thoughts in English.
