Machine Learning for Signal Processing

Postdoctoral position in neuroimaging data analysis and fusion

Start date: Summer 2022


Duration: One to four years (initial appointment is for one year)
 

Machine Learning for Signal Processing Laboratory (http://mlsp.umbc.edu) at University of Maryland Baltimore County, Baltimore, MD

Description: The postdoctoral appointment is supported by a grant from the NIH and offers salary higher than the guidelines provided by the NIH. The focus is on multimodal data fusion and study of brain dynamics using latent variable models.

The candidate should have a strong background in statistical signal processing/machine learning and a Ph.D. in Electrical Engineering, Math/Statistics, or in a related field. In addition, background
in the theory and practice of latent variable analysis methods such as blind source separation is highly desirable. Familiarity with processing of medical data, especially of fMRI is a big plus.

UMBC is an Equal Opportunity and Affirmative Action Institution.

To apply: Please send your application along with a complete CV and contact information
of at least three references to Tülay Adali at adali@umbc.edu and include "Postdoctoral position at the MLSP Lab, Summer 2022" in the subject line of your email. 

Job description

The wireless communication systems beyond 5G aim to enhance connectivity with a drastic increase in the number of connected devices and improved quality of service requirements in terms of data rate, latency, reliability, and scalability. To this end, we need new physical layer signal processing for the futuristic systems to efficiently acquire and process the resulting enormous amount of data. An important emerging approach is the deep learning-based techniques. The state-of-the-art deep learning solutions for wireless communications are indeed very promising due to their learning abilities. However, they lack interpretability and come with no performance guarantees. In this PhD project, we will investigate the integration of the traditional signal processing algorithms with performance guarantees, and the tools from deep learning, combining the best of both worlds. The challenge here is to understand the right balance between the model-driven and data-driven methods. Our goal is to explore the different combinations of the two approaches and develop new solutions for signal detection and channel acquisition. We also characterize the performance limits of this hybrid analytical framework, aiming at new wireless system designs with improved reliability and connectivity.

The Circuits and System Group seeks for enthusiastic PhD candidates to work on this project. The research will focus on

• Understanding the limitations and challenges of the signal processing for wireless technologies beyond 5G
• Developing new solutions for signal detection and channel acquisition using model-based deep learning
• Analyzing the performance guarantees of the derived solutions in terms of achievable data rates and reliability of the communication (outage and error probabilities)

Requirements

• An MSc degree in an engineering discipline relevant to PhD research.
• Strong background in applied mathematics, particularly, real analysis, probability, and linear algebra.
• Background in wireless communications is desirable, but not mandatory.
• Experience in programming e.g., Python, MATLAB, R.
• Good verbal and written English skills.
• Excellent communication and interpersonal skills.
• Ability to work in a collaborative environment.

To apply, please follow the process indicated on the formal vacancy announcement. 

Inviting applications for a rolling Ph.D. admission at Indraprastha Institute of Information Technology-Delhi (IIIT-D) on project areas in biomedical signal and image processing including cancer imaging, cancer genomics, and EEG/ECG signal processing.

No. of openings: Two

Monthly Remunerations: Rs. 31,000/- (+HRA will be provided if you are not residing on the IIITD campus)

Essential Qualifications

• B.Tech/M.Tech in ECE/CSE with outstanding academic records
• Good communication skills.
• Proficient in MATLAB and/or Python

Desired Qualifications

• UGC/CSIR JRF(Net) qualified
• Competence in machine learning/deep learning

Application Deadline: The positions will be filled as soon as suitable candidates are found.

Application Process: Send your CV via email to anubha@iiitd.ac.in . Please mention “Rolling PhD admission” in the subject line of your email.

Selected candidates will be a part of the Signal Processing and Biomedical Imaging Lab (SBILab), IIIT-D. We have many national and international collaborations. Please visit the website of SBILab for the research work being undertaken in our lab at http://sbilab.iiitd.edu.in/.

2 x Professor / Reader of Machine Learning and Artificial Intelligence

Department of Computer Science
University of Surrey
Guildford, UK

https://jobs.surrey.ac.uk/037121 

The Department of Computer Science at the University of Surrey seeks to recruit two research leaders with an international profile and an outstanding research and publication track record to lead a substantial and sustained portfolio of research in any of the following areas within Machine Learning (ML) and Artificial Intelligence (AI):

• Trustworthy AI (explainable, secure and privacy preserving machine learning)
• Deep learning for natural language processing
• Computational neuroscience and machine learning
• Distributed machine learning and optimization
• AI planning and optimal control

The Department of Computer Science has a world-class reputation in these areas and regularly publishes at top-level conferences and journals. The Department is home to the Nature Inspired Computing and Engineering (NICE) group. The group has 10 academics and holds world-leading expertise in evolutionary computation, computational neuroscience, autonomous and self-organising systems, reinforcement learning, Bayesian learning, privacy-preserving machine learning, explainable AI, computer vision, and image and natural language processing. The research group maintains close links with leading industries, the public sector, and governmental bodies, leading to a strong heritage of real-world impact.

The NICE group is a key partner in the new Surrey Institute for People-Centred AI, a major strategic investment bringing together world-leading expertise in fundamental AI theory with cross-university domain expertise to realise and shape AI impact for public good. It is expected that the postholders will play a leading role in this new Institute.

Each successful candidate will have proven success in leading multi-Faculty research proposals and securing funding through collaborative group bids. One postholder will also be considered for the strategic leadership role of the Head of the NICE group. Each postholder will also take an active role in training the ML scientists of the future by leading teaching activities at both undergraduate and postgraduate level within the University’s School of Computer Science and Electronic Engineering.

These are full-time and permanent positions. The postholders will benefit from a dynamic working environment on a leafy campus close to London, with access to world-class leisure facilities nearby. The role brings a substantial salary and generous relocation package, as well as a variety of academic and professional development opportunities.

The qualifications required and job specification is detailed in the job profile. In addition to completing the online application form, please submit:

• A two-page supporting statement on your future research and research leadership plans
• Your CV
• A list of your publications and externally funded research projects (if not included in your CV)

Interviews will be held remotely in September. Positions are available for immediate appointment and the candidates will be expected to start as early as possible after that and no later than March / April 2022.

Our staff and students come from all over the world and the Department is proud of its friendly and inclusive culture. The University and the Department specifically are committed to building a culturally diverse organisation. Applications are strongly encouraged from female and minority candidates. The Department of Computer Science was awarded a Bronze Athena SWAN award, in recognition of our commitment to equality and diversity.

Applicants are encouraged to check the associated role profile for this post. Informal enquiries are welcomed by Dr Mark Manulis (m.manulis@surrey.ac.uk). Otherwise, we look forward to receiving your online application at: https://jobs.surrey.ac.uk/037121 

Joint postdoc position between Princeton University and the Weizmann Institute at the intersection of communications, signal processing and machine learning. The work will be performed with the groups of Prof. Andrea Goldsmith and Prof. Yonina Eldar. The candidate is expected to work with both teams in collaborative and supportive environments, with the location flexible between the two centers. Interested candidates should send a CV, references and cover letter to yonina.eldar@weizmann.ac.il, goldsmith@princeton.edu.

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).

The Signal Processing (SP) research group at the Universität Hamburg in Germany is hiring a Postdoc (E13/E14) "Machine Learning for Speech and Audio Processing".

The general focus of the Signal Processing (SP) research group is on developing novel signal processing and machine learning methods for speech and multimodal signals. Applications include speech communication devices such as hearing aids and voice-controlled assistants. The research associate will do research on novel signal processing and machine learning methods applied to speech and/or multimodal signals. Furthermore the research associate will help establishing degree programs in the data science context.

Please find the full job announcement with all details here.
https://www.inf.uni-hamburg.de/en/inst/ab/sp/job-offer.html

Youtube Demos of our group can be found here.
 

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).

Title: Sparse predictive models for the analysis and classification of pathological speech

Duration: from 01/11/2021 to 31/12/2022 (could be extended to an advanced position)

Required Knowledge and background: A solid knowledge in speech/signal processing; A good mathematical background; Basics of machine learning; Programming in Matlab and Python.

Application and more information : https://jobs.inria.fr/public/classic/en/offres/2021-03570

Context and objectives : During this century, there has been an ever increasing interest in the development of objective vocal biomarkers to assist in diagnosis and monitoring of neurodegenerative diseases and, recently, respiratory diseases because of the Covid-19 pandemic. The literature is now relatively rich in methods for objective analysis of dysarthria, a class of motor speech disorders [1], where most of the effort has been made on speech impaired by Parkinson’s disease. However, relatively few studies have addressed the challenging problem of discrimination between subgroups of Parkinsonian disorders which share similar clinical symptoms, particularly is early disease stages [2]. As for the analysis of speech impaired by respiratory diseases, the field is relatively new (with existing developments in very specialized areas) but is taking a great attention since the beginning of the pandemic.

On the other hand, the large majority of existing processing methods (of pathological speech in general) still heavily rely on a core of feature estimators designed and optimized for healthy speech. There exist thus a strong need for a framework to infer/design speech features and cues which remain robust to the perturbations caused by (classes of) disordered speech. The first and main objective of this proposal is to explore the framework of sparse modeling of speech which allow a certain flexibility in the design and parameter estimation of the source-filter model of speech production. This exploration will be essentially based on theoretical advances developed by the GEOSTAT team and which have led to a significant impact in the field of image processing, not only at the scientific level [3] but also at the technological level (www.inria.fr/fr/i2s-geostat-un-innovation-lab-en-imagerie-numerique).

The second objective of this proposal is to use the resulting representations as inputs to basic machine learning algorithms in order to conceive a vocal biomarker to assist in the discrimination between subgroups of Parkinsonian disorders (Parkinson’s disease, Multiple-System Atrophy, Progressive Supranuclear Palsy) and in the monitoring of respiratory diseases (Covid-19, Asthma, COPD).

Both objectives benefit from a rich dataset of speech and other biosignals recently collected in the framework of two clinical studies in partnership with university hospitals in Bordeaux and Toulouse (for Parkinsonian disorders) and in Paris (for respiratory diseases).

References:

[1] J. Duffy. Motor Speech Disorders Substrates, Differential Diagnosis, and Management. Elsevier, 2013.

[2] J. Rusz et al. Speech disorders reflect differing pathophysiology in Parkinson's disease, progressive supranuclear palsy and multiple system atrophy. Journal of Neurology, 262(4), 2015.

[3] H. Badri. Sparse and Scale-Invariant Methods in Image Processing. PhD thesis, University of Bordeaux, France, 2015.

The Signal Processing (SP) research group at the Universität Hamburg in Germany is hiring a Postdoc (E13/E14) "Machine Learning for Speech and Audio Processing".

The general focus of the Signal Processing (SP) research group is on developing novel signal processing and machine learning methods for speech and multimodal signals. Applications include speech communication devices such as hearing aids and voice-controlled assistants. The research associate will do research on novel signal processing and machine learning methods applied to speech and multimodal signals. Furthermore the research associate will help establishing degree programs in the data science context.

Please find the full job announcement with all details here
https://www.inf.uni-hamburg.de/en/inst/ab/sp/job-offer.html