MMSP
Tampere University has several professor positions open related to AI and its applications, covering various areas of signal processing. The positions include a quite substantial starting package, covering funding for multiple research group members. Strong researchers are encouraged to apply! The deadline for applications is 9 March 2025. For more information about the positions, please visit this page.
The Signal Processing Research Centre of Tampere University is looking for a tenure track-professor (assistant/associate/full) to join its team of world-class experts. Please see the full description and apply here.
** PhD Opportunities in Centre for Doctoral Training in AI for Digital Media Inclusion
** Surrey Institute for People-Centred AI at the University of Surrey, UK, and
** StoryFutures at Royal Holloway University of London, UK
** Apply by 30 May 2024, for PhD cohort starting October 2024
URL: https://www.surrey.ac.uk/artificial-intelligence/cdt
The Centre for Doctoral Training (CDT) in AI for Digital Media Inclusion combines the world-leading expertise of the Surrey Institute for People-Centred AI at the University of Surrey, a pioneer in AI technologies for the creative industries (vision, audio, language, machine learning) and StoryFutures at Royal Holloway University of London, leader in creative production and audience experience (arts, psychology, user research, creative production).
Our vision is to deliver unique cross-disciplinary training embedded in real-world challenges and creative practice, and to address the industry need for people with responsible AI, inclusive design and creative skills. The CDT challenge-led training programme will foster a responsible AI-enabled inclusive media ecosystem with industry. By partnering with 50+ organisations, our challenge-led model will be co-designed and co-delivered with the creative industry to remove significant real-world barriers to media inclusion.
The overall learning objective of the CDT training programme is that all PhD researchers gain a cross-disciplinary understanding of fundamental AI science, inclusive design and creative industry practice, together with responsible AI research and innovation leadership, to lead the creation of future AI-enabled inclusive media.
The CDT training program will select PhD students who will work on challenge areas including Intelligent personalisation of media experiences for digital inclusion, and Generative AI for digital inclusion. Example projects related to audio include:
- Audio Generative AI from visuals as an alternative to Audio Description
- Audio orchestration for neurodivergent audiences using object-based media
- AUDItory Blending for Inclusive Listening Experiences (AUDIBLE)
- Foundational models for audio (including speech, music, sound effect) to texts in the wild
- Generative AI for natural language description of audio for the deaf and hearing impaired
- Generative AI with Creative Control, Explainability, and Accessibility
- Personalised audio editing with generative models
- Personalised subtitling for readers of different abilities
- Translation of auditory distance across alternate advanced audio formats
If you have any questions about the CDT, please contact Adrian Hilton or Polly Dalton.
For more information and to apply, visit:
https://www.surrey.ac.uk/artificial-intelligence/cdt
Application deadline: 30 May 2024
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Prof Mark D Plumbley
EPSRC Fellow in AI for Sound
Professor of Signal Processing
Centre for Vision, Speech and Signal Processing
University of Surrey, Guildford, Surrey, GU2 7XH, UK
Email: m.plumbley@surrey.ac.uk
Topic: Geometric Structure Adaptation of Wide-angle Photography
Location: Instituto de Telecomunicações, Instituto Superior Técnico, University of Lisbon, Portugal Project: Nowadays, smartphones come with a wide variety of cameras and sensors, each with a distinct function to improve the overall quality of images and enable different functionalities. These camera arrays and sensors include a primary camera with high resolution and best overall image quality, and often a wide-angle camera to provide an expanded field of view, allowing users to capture more in a single shot. This is excellent for landscape photography, architectural shots, group photos, and creative perspectives that emphasize the vastness of a visual scene. While wide-angle photography is rather powerful to capture a large field of view, it often introduces annoying geometric distortions. For example, objects closer to the camera may appear disproportionately larger than those in the background, leading to unnatural-looking images. Moreover, landscape and architectural photos obtained from wide-angle cameras often suffer from geometric distortions, such as perspective distortions, where objects near the edges of the image appear stretched or warped as well as curved or bent straight lines. Also, there should be certain consideration on captured image aesthetic while preserving the special relationships on modification. The focus of this project is on the development of efficient solutions that mitigate some of the challenges identified above. The research work will be developed in the context of a research project with a well-known company.
Location: Instituto de Telecomunicações, Instituto Superior Técnico, University of Lisbon, Portugal
Project title: Event-based Imaging
Project description: Event-based imaging, also known as neuromorphic imaging, is a recent visual information paradigm that relies on novel sensing and computing techniques that mimic how biological systems acquire and process visual information. In this paradigm, visual sensors do not capture and transmit images/frames at fixed time intervals, but rather operate on a different principle: individual pixels asynchronously respond to changes in the amount of incident light, resulting in a stream of asynchronous events. Therefore, the obtained visual data stream is sparse which makes it suitable for scenarios where very high temporal resolutions are desirable, notably in the acquisition of fast and dynamic scenes with high accuracy and low latency. Moreover, event-based imaging can efficiently handle uncontrolled lighting conditions (e.g. high dynamic range) and low-power consumption. This new visual information paradigm may therefore potentiate several advantages in many applications, such as in industrial automation, visual surveillance, augmented reality, automotive and mobile environments, drones and other applications which require fast response, high-dynamic range or low-power consumption.
The research work will be developed in the context of a European project with relevant French, Belgium and Swiss SMEs that work in the event-based imaging field.
Research grant: The research grant is associated with a yearly renewable contract that includes an experimental period of 6 months. The research grant consists of a tax-free stipend of 1144,64€ per month for PhD position plus tuitions. The candidates must fulfill the following conditions:
Well-known researchers will supervise the selected candidate: Prof. João Ascenso and Prof. Catarina Brites (http://amalia.img.lx.it.pt/index.php/team/). The candidate will join a dynamic team of senior members and PhD students where strong research and development activities in the image coding, quality assessment and 3D visual representation fields are carried out. To apply for the research grant, please submit your application by sending an email to joao.ascenso@lx.it.pt with the following documents:
Note that if the above documents are not received, your application may not be considered. Applications shall be received until 31/07/2023. Selected candidates will be submitted to one or more interviews. For any clarifications, please contact Prof. João Ascenso at joao.ascenso@lx.it.pt.
Research Engineer (Research Fellow) in Sound Sensing
Location: University of Surrey, Guildford, UK
Closing Date: Monday 08 August 2022 (23:59 BST)
Further details: https://jobs.surrey.ac.uk/025022-R
Applications are invited for a Research Engineer (Research Fellow) in Sound Sensing, to work full-time for six months on an EPSRC-funded Fellowship project "AI for Sound" (https://ai4s.surrey.ac.uk/), to start September 2022 or as soon as possible thereafter.
The aim of the project is to undertake research in computational analysis of everyday sounds, in the context of a set of real-world use cases in assisted living in the home, smart buildings, smart cities, and the creative sector.
The postholder will be responsible for designing and building the hardware and software to be developed in the fellowship, including sound sensor systems, open-source software libraries and datasets to be released from the project.
The postholder will be based in the Centre for Vision, Speech and Signal Processing (CVSSP) and work under the direction of PI (EPSRC Fellow) Prof Mark Plumbley.
The successful applicant is expected to have a postgraduate qualification in electronic engineering, computer science or a related subject, or equivalent professional experience; experience in software and hardware development relevant to signal processing or sensor devices, and experience in software development in topics such as audio signal processing, machine learning, deep learning, and/or sensor systems. Experience in development and deployment of hardware sensors, Internet-of-Things (IoT) devices, or audio systems; and programming experience using Python, C++, MATLAB, or other tools for signal processing, machine learning or deep learning is desirable. Direct research experience, or experience of hardware or software development while working closely with researchers, is also desirable.
CVSSP is an International Centre of Excellence for research in Audio-Visual Machine Perception, with 180 researchers, a grant portfolio of £26M (£17.5M EPSRC), and a turnover of £7M/annum. The Centre has state-of-the-art acoustic capture and analysis facilities and a Visual Media Lab with video and audio capture facilities supporting research in real-time video and audio processing and visualisation. CVSSP has a compute facility with 120 GPUs for deep learning and >1PB of high-speed secure storage.
The University is located in Guildford, a picturesque market town with excellent schools and amenities, and set in the beautiful Surrey Hills, an area of Outstanding Natural Beauty. London is just 35 minutes away by train, while both Heathrow and Gatwick airports are readily accessible.
For more information about the post and how to apply, please visit:
https://jobs.surrey.ac.uk/025022-R
Deadline: Monday 08 August 2022 (23:59 BST)
For informal inquiries about the position, please contact Prof Mark Plumbley (m.plumbley@surrey.ac.uk).
The AI for Sound project (https://ai4s.surrey.ac.uk/) in the Centre for Vision, Speech and Signal Processing (CVSSP) at the University of Surrey is offering the following PhD studentships in AI for Sound, available from 1 October 2021: (1) Automatic sound labelling for broadcast audio (2) Information theoretic learning for sound analysis (UK applicants) Application Deadline: 1 August 2021 CVSSP also has a number of ongoing PhD studentship opportunities for outstanding PhD candidates in all aspects of audio-visual signal processing, computer vision and machine learning, including for research related to machine learning and audio signal processing. We also welcome enquiries from self-funded and part-funded candidates. For informal enquiries on opportunities related to AI for Sound, please contact Prof Mark Plumbley (m.plumbley@surrey.ac.uk). Further information how to apply below. ----- ** PhD studentship opportunities in AI for Sound project ** (1) Automatic sound labelling for broadcast audio The aim of this project is to develop new methods for automatic labelling of sound environments and events in broadcast audio, assisting production staff to find and search through content, and helping the general public access archive content. The project will undertake a combination of interviews and user profiling, analysis of audio search datasets, and categorisation by audio experts to determine the most useful terminology for production staff and the general public as user groups.
The project will develop a taxonomy of labels, and examine the similarities and differences between each group. The project will also investigate the application of a labelled library in a production environment, examining workflows with common broadcast tools, then integrating and evaluating prototype systems. The project will also investigate methods for automatic subtitling of non-speech sounds, such as end-to-end encoder-decoder models with alignment, to directly map the acoustic signal to text sequences. Working with BBC R&D, the student will develop software tools to demonstrate the results, especially for broadcasting and the management of audiovisual archive data, and benchmark the results against human-assigned tags and descriptions of audio content. Using archive data provided by BBC R&D, the student will engage with audio production and research experts through Expert Panels, and potential end users through Focus Groups. As part of this PhD, you will have the opportunity for close day-to-day collaboration with the BBC as a member of the R&D Audio Team. Application Deadline: 1 August 2021 More information and how to apply: https://www.surrey.ac.uk/fees-and-funding/studentships/automatic-sound-labelling-broadcast-audio (2) Information theoretic learning for sound analysis (Funding Eligibility: UK applicants only) The aim of this PhD project is to investigate information theoretic methods for analysis of sounds. The Information Bottleneck (IB) method has emerged as an interesting approach to investigate learning in deep learning networks and autoencoders. This project will investigate information-theoretic approaches to analyse sound sequences, both for supervised learning methods such convolutive and recurrent networks, and unsupervised methods such as variational autoencoders. The project will also investigate direct information loss estimators, and new information-theoretic processing structures for sound processing, for example involving both feed-forward and feedback connections inspired by transfer information in biological neural networks.
Application Deadline: 1 August 2021 More information and how to apply: https://www.surrey.ac.uk/fees-and-funding/studentships/information-theoretic-learning-sound-analysis ** Other PhD studentships in the Centre for Vision, Speech and Signal Processing (CVSSP) ** CVSSP also has a number of PhD studentship opportunities for outstanding PhD candidates, including for research related to machine learning and audio signal processing. For more information see https://www.surrey.ac.uk/centre-vision-speech-signal-processing/postgraduate-research-study and scroll to "PhD studentship opportunities at CVSSP".
For informal enquiries on opportunities related to AI for Sound, please contact Prof Mark Plumbley (m.plumbley@surrey.ac.uk).
Research Fellow in Machine Learning for Sound Location: University of Surrey, Guildford, UK Closing Date: Wednesday 16 June 2021 (23:00 GMT) Applications are invited for a 3-year Research Fellow in Machine Learning for Sound, to work full-time on an EPSRC-funded Fellowship project "AI for Sound" (https://ai4s.surrey.ac.uk/), to start on 1 July 2021 or as soon as possible thereafter. We would particularly like to encourage applications from women, disabled and Black, Asian & Minority Ethnic candidates, since these groups are currently underrepresented in our area.
The aim of the project is to undertake research in computational analysis of everyday sounds, in the context of a set of real-world use cases in assisted living in the home, smart buildings, smart cities, and the creative sector. The postholder will be responsible for the core machine learning parts of the project, investigating advanced machine learning methods applied to sound signals. The postholder will be based in the Centre for Vision, Speech and Signal Processing (CVSSP) and work under the direction of PI (EPSRC Fellow) Prof Mark Plumbley. The successful applicant is expected to have a PhD (gained or near completion) in electronic engineering, computer science or a related subject; and research experience in machine learning and audio signal processing. Research experience in one or more of the following is desirable: deep learning; model compression; differential privacy; active learning; audio feature extraction; and publication of research software and/or datasets.
CVSSP is an International Centre of Excellence for research in Audio-Visual Machine Perception, with 170 researchers, a grant portfolio of £30M (£21M EPSRC) from EPSRC, EU, InnovateUK, charity and industry, and a turnover of £7M/annum. The Centre has state-of-the-art acoustic capture and analysis facilities and a Visual Media Lab with video and audio capture facilities supporting research in real-time video and audio processing and visualisation. CVSSP has a compute facility with over 200 GPUs for deep learning and >1PB of high-speed secure storage.
For more information about the posts and how to apply, please visit: https://jobs.surrey.ac.uk/026021 Deadline: Wednesday 16 June 2021 (23:00 GMT)
For informal inquiries about the position, please contact Prof Mark Plumbley (m.plumbley@surrey.ac.uk).
Face recognition has become a key technology in our society, frequently used in multiple applications, while creating an impact in terms of privacy. As face recognition solutions based on artificial intelligence (AI) are becoming popular, it is critical to fully understand and explain how these technologies work in order to make them more effective and accepted by society. In this project, we focus on the analysis of the influencing factors relevant for the final decision of an AI-based face recognition system as an essential step to understand and improve the underlying processes involved. The scientific approach pursued in the project is designed in such a way that it will be applicable to other use cases such as object detection and pattern recognition tasks in a wider set of applications. Thanks to the interdisciplinary nature of the consortium, the outcomes of XAIface will affect many fields and can be summarized as follows: (i) develop clear legal guidelines on the use and design of AI-based face recognition following the privacy-by-design approach; (ii) disentangling demographic information (age, gender, ethnicity) from the overall face representation in order to understand the impact of such traits on face recognition but also to develop demographic-free face recognition; (iii) address fairness and non-discrimination issues by following the idea of de-biasing during the training; (iv) optimize the tradeoff between interpretability and performance; (v) create tools that will allow assessment and measurement of performance and explanation of decisions of AI-based face recognition systems; (vi) analyze image coding impact to better understand how future AI-based coding solutions may be different from a recognition explainability point of view.
This project includes several international teams and will last for 3 years. The working place will be at Instituto de Telecomunicações, Instituto Superior Técnico, Lisboa, Portugal.
Research grant: The research grant is associated to a yearly renewable contract (up to 3 years) that includes an experimental period of 6 months. The research grant consists on a tax-free stipend of 1616€ per month. The candidates must fulfill the following conditions:
The selected candidate will work in a team lead by Prof. Fernando Pereira and Prof. João Ascenso (see http://www.img.lx.it.pt/Staff.html for details). The candidates will join a team of staff and PhD students where intense research and development activities in the multimedia signal processing and machine learning fields are carried out.
To apply, please submit your application by sending an email to Prof. Fernando Pereira and Prof. João Ascenso at fp@lx.it.pt and joao.ascenso@lx.it.pt with the following documents:
Applications shall be received until suitable candidates are found but before 15/2/2021. Selected candidates will be interviewed. For any clarifications, please contact Prof. Fernando Pereira and Prof. João Ascenso.
A postdoctoral scholar position with a focus on applications of machine learning in cardiac MRI. Details can be found at:
https://recruit.ap.uci.edu/JPF05862
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