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Self-supervised Machine Learning from Multiple Sensory Data

University of Birmingham - School of Computer Science

Qualification Type: PhD
Location: Birmingham
Funding for: UK Students, EU Students, International Students
Funding amount: Not Specified
Hours: Full Time
Placed On: 27th September 2022
Closes: 15th November 2022

Machine Learning or more specifically, Deep Learning has made great progress in many areas including but not limited to computer vision, natural language processing, audio data processing, healthcare, science etc., demonstrating performance even better than human experts. However, success heavily relies on manually annotated ground-truth data from human experts to train the deep models. Obtaining high-quality labelled data requires a huge amount of manpower and financial resources and in many situations needs domain knowledge. Such limitations also restrict any developed deep model to a particular application scenario and prevents its power from being generalised or transferred to other applications.

This project aims to study and explore the potential of learning general transferable representations from multi-modal data in a self-supervised manner. The multi-modal data here means data from multiple sensors. For example, potential data modalities could be image, video, audio, text, 3D depth, multi-view, geographical information and other metadata. The target general transferable representations indicate the knowledge learned by the deep model that can be well transferred to downstream tasks. For example, a model was pre-trained on a large-scale dataset for task A, and then applied to tasks B, C, etc. without requiring additional effort on data from tasks B and C. This is important as it can greatly alleviate the cost of building AI models. Multi-modal data is also beneficial for self-supervised representation learning as it provides more constraints and consistency among different modalities. The student is expected to start by working on public datasets available in the community. Deep learning models will be developed by the student to take multi-modal data as input and generate the high-quality representations as mentioned above. In a later stage, a new dataset would be constructed and novel algorithms will be developed upon that to answer the challenging questions within this topic and beyond.

Essential Knowledge and Skills:

Strong programming skills (Python, C, Matlab, etc.)

Strong mathematical knowledge and background

Desirable Experiences:

Practical experience with deep learning frameworks (e.g. PyTorch and TensorFlow)

Experience with Computer Vision projects

Experience with Audio or NLP processing

Knowledge of self-supervised learning, CNNs, transformers, geometry, computer graphics rendering models

Experience with data collection

Experience with scientific paper writing (e.g. publication or submission

We want our PhD student cohorts to reflect our diverse society. UoB is therefore committed to widening the diversity of our PhD student cohorts. UoB studentships are open to all and we particularly welcome applications from under-represented groups, including, but not limited to BAME, disabled and neuro-diverse candidates. We also welcome applications for part-time study.

Eligibility: First or Upper Second Class Honours undergraduate degree and/or postgraduate degree with Distinction (or an international equivalent). We also consider applicants from diverse backgrounds that have provided them with equally rich relevant experience and knowledge. Full-time and part-time study modes are available.

We will consider applications from students wishing to start during the 2022-23 academic year or who wish to begin their studies in autumn 2023.

The position offered is for three and a half years full-time study. The value of the award is stipend; £16,602 pa; tuition fee: £4,596 pa. Awards are usually incremented on 1 October each following year.

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