Signal Processing Theory and Methods

• Lab: L2S, CentraleSupélec  3 rue Joliot Curie, 91192 Gif-sur-Yvette, France
• Supervisors: Gilles Chardon (gilles.chardon@centralesupelec.fr), José Picheral (jose.picheral@centralesupelec.fr)
• Starting date: before december 2022
• Duration: 12 months
• Gross Salary: 3200€ monthly
• Profile: signal processing, optimization, data science, knowledge of Matlab or Scipy. Knowledge of inverse problem or acoustics is appreciated but not necessary.

For details, see the position description on Lab Web Site

3-year Postdoc + PhD students in Network information-theoretic sensor management for multi-target surveillance

These positions are funded by the Centre Interdisciplinaire d’Etudes pour la Défense et la Sécurité, l’Agence de l’innovation de défense (AID), DGA. 

The objective of the project is to develop an approach to adaptively allocate sensing resources in multi-sensor multi-target tracking surveillance networks based on fundamental concepts in network information theory and decision-theoretic criteria.

Motivation: The objective of this project is to develop the underpinning methods required for autonomous distributed sensor management and fusion in challenging multi-target environments. The tools developed will help reduce the labour-intensive burden of monitoring single sensor feeds, and enable adaptive decisions to be taken to optimise the operation of multimodal networks and enhance the overall knowledge of the surveillance region. The focus on information-theoretic representations of multi-target tracking scenarios will enable verification of whether sensor feeds can be reliably fused, to avoid the potential of data corruption. The project will deliver key advanced in intelligent sensing to enable the continuous and adaptive surveillance in dynamic environments. These will be scaleable for large-scale tracking of many targets from multiple distributed sensors.

The project will involve the development of algorithms that are able to automatically track multiple targets, classify, and allocate resources based on information received from multiple platforms with data association uncertainty and high false-alarm rates. Building on recent developments by the investigator in multi-target tracking and distributed sensor fusion, this work programme will develop methods for autonomous sensor allocation in large-scale multi-sensor multi-target tracking applications based on information-theoretic criteria. This will be achieved by re-evaluating the key tools in information theory applied to the challenges of multi-target surveillance based on point process theory, which is designed to accommodate uncertainty in the states of individual targets and the target number. The information-theoretic methods developed will be applied to multi-sensor problems to enable decisions to be made on how to allocate sensor resources in addition to refining the knowledge of the scene.

ACTIVITIES :

Deliverables:

(i) The primary outcomes of the work will be the preparation of a journal article for a leading IEEE Transactions journal, such as Information Theory, Signal processing, or Aerospace and Electronic Systems.

(ii) Development of sensor management algorithm(s)

(iii) Report document which details the sensor management algorithm(s) and a detailed technical description of how the benefit of these are quantified

(iv) Code routines demonstrating the utility of a sensor management algorithm developed.

Publication:

The key scientific outcome of the scientific work will be the preparation of a journal article for a leading IEEE Transactions journal, such as Information Theory, Signal processing, or Aerospace and Electronic Systems. Additionally, the work will be disseminated at a leading international conference such as the ISIF International Conference on Information Fusion.

REFERENCES:

[1] Multi-Sensor Network Information for Linear-Gaussian Multi-Target Tracking Systems, DE Clark,  IEEE Transactions on Signal Processing 69, 4312-4325 2021

[2] A Formulation of the Adversarial Risk for Multi-object Filtering A Narykov, E Delande, DE Clark,  IEEE Transactions on Aerospace and Electronic Systems 57 (4), 2082-2092

[3] An Algorithm for Large-Scale Multitarget Tracking and Parameter Estimation, MA Campbell, DE Clark, F de Melo, IEEE Transactions on Aerospace and Electronic Systems 57 (4), 2053-2066

APPLICATION:

Application deadline: June 12, 2022

contact: daniel.clark@telecom-sudparis.eu

A full professor position is opened in Statistical Signal Processing at University Paris-Saclay and L2S, France.

Teaching

Teaching in signal processing in the Bachelor and Master programs in Electrical Engineering (E3A) of the Faculté des Sciences d’Orsay, and occasionally, in the engineering program of Polytech Paris-Saclay.

Pedagogical goals and needs for training

The candidate will play a leading role in the construction and organization of courses related to signal processing within the Bachelor and Master programs in Electrical Engineering (E3A) of the Faculté des Sciences d’Orsay, and occasionally, in the engineering program of Polytech Paris-Saclay. The ability to teach in various areas (mathematics, signal and image processing, artificial intelligence for engineering, telecommunications) is appreciated. She/he is expected to run one of the Electrical Engineering master program tracks, manage teaching teams, and work with academic and industrial partners. Finally, she/he will be involved in the internal structures of the University Paris-Saclay: Engineering Graduate School or Computer Science Graduate School, and the Interdisciplinary Objects.

Research activities

The recruited candidate will conduct research at L2S and propose a research project in line with the Signals and Statistics teams: the Modeling and Estimation Group and the Inverse Problem Group. Research topics cover fundamental and applied issues at the heart of data science, with applications in various fields such as health, energy, and the industry of the future. In these fields, the data are complex (massive, heterogeneous, distributed) with significant modeling needs to extract useful information, quantify uncertainties, and high-performance computing challenges.

The L2S researchers in statistical signal processing are in charge of methodological developments for analyzing massive, heterogeneous, temporally and spatially correlated data, for solving inverse problems, and for the optimal design of experiments. The proposed methods and algorithms exploit multivariate statistics, numerical optimization, statistical inference, and representation learning expertise. The L2S researchers deploy their knowledge in the framework of large-scale collaborative projects at the academic and industrial levels.

The recruited candidate will conduct research at the highest level in statistical signal and image processing. She/he will have an internationally recognized expertise entirely in line with current research issues in data science. She/he will contribute to developing the statistical learning theme in L2S, which has become ubiquitous in information sciences. In this sense, she/he will reinforce the L2S position as a significant player at the national and international levels.   

The recruited candidate will be a driving force in setting up research projects. She/he is expected to develop collaborations on societal issues (environment, health, transport, energy, artificial intelligence, etc.). She/he will be strongly involved in the life of L2S and scientific animation. She/he will benefit from the environment of University of Paris-Saclay.

Keywords

Signal and image processing, statistical learning, inverse problems, statistical analysis of multivariate signals, massive data processing.

Complete profile:
https://www.galaxie.enseignementsup-recherche.gouv.fr/ensup/ListesPostesPublies/ANTEE/2022_1/0912408Y/FOPC_0912408Y_169.pdf

Interested candidates should contact:

• Sophie Kazamias (sophie.kazamias@universite-paris-saclay.fr): teaching
• Charles Soussen (charles.soussen@centralesupelec.fr): research

Presentation of the activities and context of the position:

Télécom SudParis is recruiting an Assistant or Associate Professor in Statistical Signal Processing. The strategic objective of recruitment is to meet the demand for training for specialists in the theory and application of signal processing in the fast-growing fields of data science, signal processing, information theory, and digital communications.

The candidate’s research focus will be in the field of probabilistic modelling and statistical signal processing in a broad sense. Applications related to fundamental research in the fields of the theory of signal processing, information theory, system identification, or optimization will be welcome. Expertise in areas such as statistical models for secure communications and smart sensors, would also be appreciated.

The position will be part of the specific action Data Science and Artificial Intelligence of the SAMOVAR Laboratory, the Flagship Theme Industry of the Future of IMT, as well as the Interdisciplinary Centers of IP Paris.

The future recruit will be called upon to participate in the responses to national or European calls for projects, as well as in the setting up of academic collaborations with partner schools in the Institut Polytechnique de Paris (Ecole Polytechnique, ENSTA Paris, ENSAE ParisTech and Télécom Paris)  and industry e.g. Thales, SAFRAN, etc. …

The future recruit will provide core and specialist teaching in signal processing, digital communications, statistics, probability for the various teaching programs of the school, including the Engineering Diploma, Engineering Apprenticeship, and the specialist Masters programme.  In particular, they will invest in the assembly and framing of the tutorial and pedagogical material for the students;  participate in the supervision of students' projects and doctoral theses; and offer courses at the M2 level in her field of expertise. Courses will be delivered in French and English.

Télécom SudParis (www.telecom-sudparis.eu) is a member of the Institut Polytechnique de Paris (ip-paris.fr) and the Institut Mines-Télécom (www.imt.fr). Located on two campuses in Evry and Palaiseau, the school offers an excellent environment for research and teaching. The future recruit will join the "Communications, Images, and Information Processing" department (CITI) and will be part of the "Information Processing for Images and Communications" (TIPIC) team of the SAMOVAR laboratory. At the Institut Polytechnique de Paris (IP Paris) they will be part of one of the "Mathematics" or "Information, Communication, and Electronics" departments.

Job Requirements:

Levels of training/skills:

- Doctorate level qualification i.e. PhD/DPhil

- Civil servant (category A) belonging to a body recruited through the Ecole Polytechnique or the ENA or former student of a Ecole Normale Supérieure with a professional experience of more than 3 years

- Categories or occupations of IMT agents who can apply: II – C/D/E/P/R/T

Essential skills:

- Experience or international visibility

- Fluency in English and French

- Teaching and research experience

Capabilities:

- Teamwork, interpersonal skills 

- Pedagogical qualities

- Ability to write and synthesize

Application procedures:

Applications must be submitted via the career site: https://institutminestelecom.recruitee.com/o/maitre-de-conferences-en-mathematiques-appliquees-probabilites-et-statistiques-fh

The application must include:

- a detailed CV including a list of teaching activities and a list of the main publications

- a description of the planned teaching and research activities

- the contact details of two referees

- any material deemed interesting by the candidate to demonstrate his abilities

Application deadline: March 31, 2022

Envisaged interview period: April/May/June 2021, either physically or remotely

Expected start date (negotiable): September 1, 2021

Category and occupation of the position in the IMT management framework: II – C, name Maitre de Conferences

Applicants are strongly encouraged to contact the department to discuss and define their teaching and research projects:

• Wojciech PIECZYNSKI (wojciech.pieczynski@telecom-sudparis.eu), Director of the CITI Department
• Daniel Clark (daniel.clark@telecom-sudparis.eu), Director of the TIPIC Laboratory

About the school:

Télécom SudParis is a public engineering school recognized at the highest level of digital science and technology. The quality of its training is based on the scientific excellence of its faculty and a pedagogy emphasizing team projects, disruptive innovation and entrepreneurship. Télécom SudParis has 1,000 students, including 700 engineering students and about 100 doctoral students. Télécom SudParis is part of the Institut Mines-Télécom, the leading engineering school group in France, and shares its campus with Institut Mines-Télécom Business School. Télécom SudParis is co-founder of the Institut Polytechnique de Paris (IP Paris), a world-class Institute of Science and Technology with Ecole Polytechnique, ENSTA Paris, ENSAE ParisTech and Télécom Paris.

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 

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).

Joint postdoc position between the Broad Institute and the Weizmann Institute at the intersection of signal processing, machine learning, and healthcare. The work will be performed with the groups of Prof. Yonina Eldar and Dr. Anthony Philippakis at the Biomedical Engineering Center at Weizmann and the Eric and Wendy Schmidt Center at the Broad. 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, aphilipp@broadinstitute.org.

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).

The Signal Acquisition, Modeling, Processing and Learning (SAMPL) lab headed by Prof. Yonina Eldar at the Weizmann Institute of Science is recruiting post-doctoral students for cutting-edge research

at the intersection of signal processing, information theory and learning.  The work will be performed in collaboration with Prof. Muriel Medard at MIT, working with collaborative and supportive teams.

Background in one or more of the above areas required with the desire to expand into the other areas.

Interested candidates should send a CV, references and cover letter to yonina.eldar@weizmann.ac.il.

The Signal Acquisition, Modeling, Processing and Learning (SAMPL) lab headed by Prof. Yonina Eldar at the Weizmann Institute of Science is recruiting post-doctoral students for cutting-edge research

at the intersection of signal processing, information theory and learning.  The work will be performed in collaboration with Prof. Muriel Medard at MIT, working with collaborative and supportive teams.

Background in one or more of the above areas required with the desire to expand into the other areas.

Interested candidates should send a CV, references and cover letter to yonina.eldar@wiezmann.ac.il.