Machine Learning for Signal Processing

Audience:  Fresh PhD who want to apply their ML skill to develop innovative applications in audio and music, with direct implementation within a commercialized product, and an ambitious technological roadmap for the years to come.

Job description: full time job within a team of 4 engineer / researchers / developers, completely integrated with the 12 members of the company.

NoMadMusic (www.nomadmusic.fr) is a classical, jazz and world music record label focused on offering interactive music through audio source separation methods and interactive scores.

The application NomadPlay (https://nomadplay-app.com/) gives to every musician the opportunity to play with famous orchestras and artists.

The project has been awarded the Paris Innovation Grand Prix in 2018, the Art Tech Prize 2018 in Geneva and the Music Education Prize by the Chambre Syndicale des Editeurs de Musique de France.

In 2018, after 5 years of development, NoMadMusic has raised funds from private and public investors in order to develop its catalog, its technology and to be ready for a go to market in September 2019.

Today, NoMadMusic is recruiting a AI research engineer to join its team based in Paris, France. 

Among the R&D team, your main tasks will be:
- Developing and improving audio processing and source separation algorithms
- Tests and validation of code
- Production of multitracks music using developed source separation algorithm
- Bibliographic research and attending scientific conferences 
- Contributing to the design of innovative musical solutions

Your profile

- PhD in machine learning/source separation
- Experience in algorithms development of programming for audio processing
- Knowledge of neural networks, non-negative matrix factorisation, principal component analysis, 
   bayesian methods, music information retrieval, speech processing 
- Technical environment: Python, Matlab. Linux, Bash, Git are a plus
- Fluent in English
- Interest in music would be a plus but is not required

When? 

Starting Q4 2019
Salary depending on the profile, degree and experience. 
Please send your application to: thomastassin@nomadmusic.fr

 

Educational Testing Service (ETS), with headquarters in Princeton, NJ, is the world’s premier educational measurement institution and a leader in educational research. With more than 3,400 global employees, we develop, administer and score more than 50 million tests annually in more than 180 countries at more than 9,000 locations worldwide. We design our assessments with industry-leading insight, rigorous research and an uncompromising commitment to quality so that we can help education and workplace communities make informed decisions.

ETS's Research & Development division has an opening for an associate research scientist in the Speech area of the NLP & Speech research center. The projects in this research group focus on the application of NLP, speech, dialogue, and multimodal processing algorithms in automated scoring capabilities for assessment and learning tasks involving constructed responses (such as essays and spoken responses). In addition to its signature automated scoring systems for text and speech assessments, the group is actively pursuing technology innovation in education and learning spaces by performing foundational research, prototyping next-generation capabilities, and collaborating with academic and industry partners. The NLP & Speech research group currently comprises around 20 PhD-level research scientists, 10 research engineers, and 10 research assistants and administrative staff.

This is an excellent opportunity to be part of a world-renowned research and development team and have a significant impact on existing and next-generation NLP, speech, dialogue, and multimodal systems and their use in educational applications.

Responsibilities:

• Conceptualizing, proposing, obtaining funding for, and directing small projects in the area speech processing for educational applications and assisting in moderate-to-major research projects. 
• Assist in generating or contributing to new knowledge or capabilities in the field of speech processing and in applying that new knowledge and capabilities to existing and/or new ETS products and services.  Some speech processing research areas that are relevant to ETS R&D include (but are not limited to) automatic speech recognition for non-native speakers of English, voice biometrics, computer-assisted pronunciation training, and automated speech scoring.
• Participate in setting substantive research and development goals and priorities for a group or initiative within a vice presidential area.
• Develop proposals and budgets for small projects and/or assist in development for moderate-to-major ones.

Requires:

• Ph.D. in Computer Science, Computational Linguistics, Electrical Engineering, Natural Language Processing, Linguistics, or a similar area with major education in speech processing.
• At least one year of independent substantive research experience in speech processing is required. Experience can be gained through doctoral studies.
• Practical expertise with speech processing tools (e.g., kaldi), experience with machine learning toolkits (e.g., Weka, scikit-learn), and fluency in at least one major programming language (e.g. Java, Python).
• Practical experience with deep learning paradigms and toolkits (e.g., TensorFlow, pytorch, Keras) is highly desirable.
• Documented contributions to research communities and a strong publication record and experience I system building and prototyping are preferred.

 We offer a competitive salary, comprehensive benefits, possible relocation assistance and excellent opportunities for professional and personal growth. For a full list of position responsibilities and to apply please visit the following link: Associate Research Scientist, Speech

EDUCATIONAL TESTING SERVICE is an Equal Opportunity and Affirmative Action Employer of Women and Minorities.

EDUCATIONAL TESTING SERVICE is an Equal Opportunity and Affirmative Action Employer of protected Veterans and Individuals with Disabilities.

EDUCATIONAL TESTING SERVICE is a Drug-free workplace.

P2IRC Computer Science Research Positions

Posted: August 2, 2019

The Plant Phenotyping and Imaging Research Centre (P2IRC) is a computational and agricultural research centre managed by the Global Institute for Food Security (GIFS) and located at the University of Saskatchewan. P2IRC was established thanks to funding awarded to the University of Saskatchewan by the Canada First Research Excellence Fund award, Designing Crops for Global Food Security.  GIFS (www.gifs.ca) was founded in 2012 to perform research that will help deliver transformative innovation to agriculture in both the developed and the developing world. The Department of Computer Science is the home of 26 faculty, 10 staff, and more than 130 graduate students and postdoctoral fellows working in diverse areas of computer science.

P2IRC’s seven-year transdisciplinary program will transform crop breeding through research in phenomics, genomics, imaging, and data analysis in order to transform plant breeding and agricultural practices. P2IRC (http://p2irc.usask.ca/) is a major research centre with partners located on campus, across Canada, and internationally.

Salary Information:

Salaries will be based on training, education, and experience, but will fall within the following ranges:

• Graduate Students: $20,000 - $23,000 per year
• Post Doctoral Fellows: $50,000 - $60,000 per year
• Research Associates: $40,000 - $90,000 per year

Primary Purpose:

We are searching for bright and enthusiastic individuals to join our team as a graduate students, post-doctoral fellows, or programmers in the areas of machine learning, computer vision, data science, data management, software development and web development. You will join a large team of researchers developing state-of-the-art computational techniques and tools that will transform agriculture and plant breeding over the next decade.

The ideal candidates will have strong computer programming skills and a keen interest in the application of computer science to agriculture and plant or soil science. We are looking for new members to join data and image analysis efforts in the Flagship research projects, as well as data management and software tool development to support P2IRC researchers.

Application procedure:

1. Complete an application form: https://bit.ly/31bmaDz
2. Send an email indicating your interest and experience, a CV, and a pdf of your transcripts to: p2irc.compsci@gmail.com

Nature of Work/Research:

We are hiring for the following positions

• Machine learning and computer vision
• Machine learning and data science
• Databases and data management
• Frontend web development
• Software Engineer/Developer

Accountabilities:

Activities are dependent on the position area, but generally will include:

• Research into state-of-the-art computational techniques for data analysis and/or data management;
• Collaborate closely with plant and soil science researchers within and external to P2IRC;
• Develop professional-level software with modern software development practices; and
• Written communication for research publications and software documentation.

Education: Graduate student and RA applicants require a Bachelor’s degree in computer science or data science. Postdoctoral Fellow applicants require a PhD in computer science or related field, awarded within 5 years immediately preceding the appointment.

Experience: Previous experience with machine learning, convolutional neural networks, and/or large software development projects is required. Previous experience working in plant/soil science and agricultural application domains is an asset.

Skills:

• Research motivation, good command of English, and excellent communication
• Excellent programming skills and ability to rapidly understand different data analysis approaches; experience contributing to large software platforms is a plus
• Knowledge of Python and deep learning frameworks
• Familiarity with data repositories of genomic and phenotypic information

Start date and duration:

Expected start date is September 1, 2019. Expected duration is 12 months, renewable for up to 3 years.

Supervision and Academic Unit:

Positions will be held with the Dept. of Computer Science and supervised by P2IRC and Computer Science investigators.

Application Timeline:

The application deadline is 19 August 2019. We will continue to review applications after 19 August 2019 until all positions are filled. Only successful candidates will be contacted.

 

Applications are invited for a postdoctoral position in the area of machine learning and data analytics for human performance understanding and prediction, a collaborative effort at Tufts University among the Department of Electrical and Computer Engineering, Department of Computer Science, the Center for Applied Brain and Cognitive Sciences (CABCS) at Tufts University and the U.S. Army Combat Capabilities Development Command Soldier Center at Natick., MA This appointment would be for 12-18 months with an estimated start date of October 2019.

The primary project is entitled “Real time prediction of individual and team performance metric from neurophysiological measurements and team interaction data”. Under this project, the fellow will work with Tufts faculty, Drs. Shuchin Aeron, Michael Hughes, and Eric Miller, as well as CABCS scientists to develop supervised and semi-supervised machine learning algorithms that are capable of predicting cognitive state (e.g. stress) and task performance metrics (e.g. speed or marksmanship) from labeled and unlabeled multimodal physiological sensor data including information collected continuously as a function of time (e.g. accelerometer recordings or GPS trajectories) as well as data at a relatively few points in time before, during, and after a specific task (e.g. surveys and performance evaluations). 

In addition to assessing individuals, data will be collected to support the characterization of team and intergroup dynamics. We anticipate the effort will require the use of classical as well as recent developments in machine learning and in particular recurrent neural networks, deep generative models, manifold learning, and social network analysis. 

While previous experience in theoretical and applied machine learning would be ideal, we welcome applicants with significant experience in related fields including information theory, statistical signal processing, sparse signal or image processing, compressive sensing, and distributed convex optimization.   

Online Application Only:http://emea3.mrted.ly/28f97

The University of Luxembourg is a multilingual, international research university.

The Interdisciplinary Centre for Security, Reliability and Trust (SnT) invites applications from PhD holders for conducting research in design and optimization methods for performance enhancement of next generation radar networks

SnT is carrying out interdisciplinary research in secure, reliable and trustworthy ICT systems and services, often in collaboration with industrial, governmental or international partners. The SIGCOM group in SnT is pursuing research on automotive radar applications in partnership with IEE (www.iee.lu), a Luxembourg based global leader in automotive safety sensing systems for occupant detection and classification. Recently, Prof. Bjorn Ottersten Director of SnT and head of SIGCOM, has been awarded the prestigious European Research Council (ERC) Advanced Grant to pursue research on cognitive radar systems with applications to automotive radar. For further information, you may visit www.securityandtrust.lu and http://wwwen.uni.lu/snt/research/sigcom.

Project Description

As novel applications emerge, the requirements on radar systems have grown significantly from being “a blip on the radar”, to providing an image like reconstruction of the surroundings. Currently, multi-static and widely-separated MIMO radars offer multi-view perspective. However, these systems suffer from the need for high rate synchronization, lack of performance guarantees, minimal exploitation of advances in waveform processing and machine learning among others. Thus, it is essential to go beyond the mature co-located MIMO and the current widely-separated MIMO radars towards achieving reliable imaging like performance for extended targets. In fact, many of these networks, such as in the automotive scenario, can involve large number of dynamic nodes. This necessitates devising novel radar-network architectures as well as exploring various optimization methodologies for waveform design, super-resolution parameter estimation and decentralized resource allocation.  

This emerging field opens interesting avenues for pursuing research in radar signal processing, especially on

• Relevant architectures for novel radar networks including information exchange mechanisms for identified use cases
• Associated signal processing elements including development of optimization algorithms for waveform, receiver design including high-resolution parameter estimation as well as their adaptation to dynamic scenarios
• System optimization using model based mechanisms as well as machine learning approaches; deep unfolding applications

The research associates will have further the opportunity to work towards in-lab demonstration of the developed techniques using USRP/SDR implementations.

The SIGCOM research group is in a unique position towards realizing the objectives of the project having exposure to radar signal processing through ongoing research projects, evolution of communication standards through participation and contribution as well as experience with prototype chip sets from the test-bench development activity.

Your Role

The successful candidates will join a strong and motivated research team lead by Prof. Björn Ottersten in order to carry out research in the area of signal processing for next generation radar systems.

The position holder will be required to perform the following tasks:

• Shaping research directions in line with project objectives, pursuing research and delivering project outputs
  • Carrying out cutting edge research activities in architectural definition, waveform design and receiver processing, enabling cognition in next generation radar networks
  • Participating in development and upgradation of the SDR based Radar test bench based on pursued research is considered as a plus
• Disseminating the results through scientific publications in high impact factor journals
• Presenting the results in the internationally well-known conferences and workshops
• Attracting funding in cooperation with partners
• Providing guidance to PhD and MSc students
• Assisting in teaching duties
• Organizing relevant workshops

For further information, please contact Bhavani.Shankar@uni.lu or Bjorn.Ottersten@uni.lu

Your Profile

Qualification: The candidate should possess (or be in the process of completing) a PhD degree or equivalent in Electrical/Electronics Engineering, Computer Science or Applied Mathematics.

Experience: The ideal candidate should have research project-based experience (FP7/H2020, Industry) and publication record in a number of the following topics:

• Optimization methodologies with application to Radar Systems
• Machine/Deep Learning with applications to Radar Systems
• Widely-separated MIMO Radar System, Waveform Design and Receiver processing
• Statistical Signal Processing

Exposure to USRP/SDR implementation and familiarity with FPGA programming is considered as an advantage. Development skills in one of the programming languages, MATLAB, LabVIEW or C++ are required.

Exposure to the latest radar technology and digital communications is desirable.

Language Skills: Fluent written and verbal communication skills in English are required.

We offer

The University offers a two-year employment contract that may be extended up to five years. The University is an equal opportunity employer. You will work in an exciting international environment and will have the opportunity to participate in the development of a newly created university.

Further Information

Application should be submitted online and include:

• Full CV, including list of publications and name (and email address, etc.) of three referees
• Transcript of all modules and results from university-level courses taken
• Research statement and topics of particular interest to the candidate (300 words)

Deadline for applications:  September 15, 2019.  Applications will be processed as they arrive; early application is highly encouraged.

Postdoctoral Fellow in Machine Learning for Modeling Alzheimer's Disease Development

We seek a post-doctoral research fellow to work on building machine learned models of Alzheimer’s disease (AD) onset and progression, using a large repository of healthcare data (www.ices.on.ca). The PDF will work collaboratively with an interdisciplinary team comprising five faculty members (Drs. Geoff Chan, Dallas Seitz, Gunnar Blohm, Colleen Maxwell, and Xiaodan Zhu) and other research staff on the project. The team has expertise in machine learning and deep learning, geriatric mental health, pharmacology, and neural science. For the project, some baseline models have been built. The position provides excellent opportunities for the PDF to gain experience, hone expertise, and contribute to unleashing the power of machine learning on healthcare data in order to treat and prevent a disease with increasing socioeconomic consequences.

Start Date and Duration of Appointment

September 1, 2019 - August 31, 2020 (possibility of renewal for one additional year). There’s some flexibility with the start date, though earlier start is preferred.

Qualifications

Consideration will be given to applicants who (will) have completed their Ph.D. degrees by the start date, with machine learning (ML) as a key component of his/her recent research. Experience in building machine learned models using datasets in health and biological disciplines, such as medicine, pharmacology, epidemiology, and genomics, would be an asset. Applicants with experience in other ML application domains are welcome. An important consideration is the candidate’s ability to adapt quickly to working with ICES data and the team to achieve project goals.

Remuneration

Competitive salary (plus benefits) commensurate with qualification and exceeding the salary provision in Queen’s PDF union collective agreement.

Supervision

The PDF will be mentored by faculty members on the team. Administratively, the PDF reports to Dr. Geoffrey Chan, Department of Electrical & Computer Engineering, and Dr. Dallas Seitz, Division of Geriatric Psychiatry, Department of Psychiatry.

Application Procedure

Interested applicants please email a copy of your current CV, academic transcripts, and the names and contact information of three professional references to Dr. Geoffrey Chan (chan@queensu.ca). Questions related to this position should also be directed to Dr. Chan by email. Applications will be considered until the position has been filled. 

EMPLOYMENT EQUITY: The University invites applications from all qualified individuals. Queen's is committed to employment equity and diversity in the workplace and welcomes applications from women, visible minorities, Aboriginal peoples, persons with disabilities, and LGBTQ persons.

ACCOMMODATION IN THE WORKPLACE: The University has policies in place to support its employees with disabilities, including an Accommodation in the Workplace Policy and a policy on the provision of job accommodations that take into account an employee's accessibility needs due to disability. The University will provide support in its recruitment processes to applicants with disabilities, including accommodation that takes into account an applicant's accessibility needs. If you require accommodation during the interview process, please contact Geoff Chan at chan@queensu,ca, 613-533-2939.

Motivated by Shannon's channel coding theorem, error-correction coding has become an integral part of all modern communications systems and standards that have enabled the information revolution of the past decades. Recently, there has been a growing interest in mission-critical applications that have extremely stringent reliability and latency constraints, such as autonomous driving, industrial automation, and remote robotic surgeries. The systems used in these applications are referred to as ultra-reliable low-latency communications (URLLC) systems in the context of the newly-introduced 5G communications standard. The most common way to enable ultra-low-latency communications is to use very short data packets that are only, e.g., 64 or 128 bits long. Unfortunately, modern error-correcting codes are typically designed for (and are hence most effective for) long data packets. For this reason, the design of error-correcting codes and corresponding decoding algorithms that specifically target short packets is a research topic that is garnering significant attention.

For example, the short packets that are used in URLLC systems enable the use of novel and exotic decoding algorithms with very strong reliability guarantees that would otherwise be infeasible in terms of their implementation complexity. The efficient hardware implementation of these decoding algorithms as well as their adaptation to specific classes of error-correcting codes is an important open research direction. Moreover, the design of optimal (semi-)random error-correcting codes and decoders using non-linear optimization or machine learning techniques also becomes feasible when using short packets.

The general focus of this project is on the design of error-correction coding schemes, decoding algorithms, and hardware architectures for URLLC systems. The successful candidate for this PhD position will have significant freedom (and will receive the appropriate guidance) to shape the exact research agenda of the project.

Job requirements

We are looking for candidates that match the following profile:

• A master’s degree (or equivalent) in Electrical Engineering or related disciplines
• Good knowledge of communications systems and, in particular, error-correcting codes.
• Good knowledge of hardware design on an RTL level (e.g., VHDL or Verilog).
• General working knowledge of programming (e.g., MATLAB, Python).
• Familiarity with ASIC design (e.g., synthesis, place and route) would be beneficial.
• Familiarity with machine learning techniques and tools would be beneficial.
• Good communication and organization skills, ability to work in a team, positive and proactive problem-solving attitude.
• Excellent English language skills (writing and presenting).

Details on the conditions of employment and the application method can be found here.

A PhD position with full scholarship is available in our group at the Nanyang Technological University, Singapore (http://www.ntu.edu.sg/home/wptay/index.html) starting January 2020. Our group's main research interests are in information and signal processing for networks, with particular emphasis in network inference and estimation techniques, distributed signal processing, machine learning, social learning, applied probability and statistics. 

The candidate should meet following criteria:

1. Has a BSc/MSc in Electrical Engineering, Computer Science, or Mathematics.
2. Has a strong mathematics background.
3. Has independent thinking and is passionate about research.

Please send your CV and transcript to wptay@ntu.edu.sg.

best regards,

Wee Peng Tay

The Speech Technology Group of Toshiba Research Europe in Cambridge is looking for exceptional candidates to join our team of researchers, working in automatic speech recognition or statistical dialogue systems. We are looking for candidates with background in signal processing, machine learning, acoustic modelling or expertise in building state-of-the-art systems for ASR or Dialogue. 

The candidate should have a PhD or Masters with equivalent experience in the areas of speech technology related to automatic speech recognition, statistical dialogue modelling, machine learning or a related field (Post-doctoral/industrial experience is beneficial).  The candidate has good programming skills with Python and/or C/C++ and proficient with Unix/Linux. Experience with speech and/or machine learning toolkits, e.g. Kaldi, TensorFlow, PyDial, PyTorch, etc. would be beneficial.

For more information please visit: https://www.toshiba.eu/eu/Cambridge-Research-Laboratory/Speech-Technology or alternatively send your CV and covering letter to: stg-res-jobs@crl.toshiba.co.uk

Research Engineer-Voice Biometrics (VB) / Speaker Recognition (SR), Shanghai, China

Overview:

Nuance Automotive specializes in conversational AI technologies for car manufacturers, helping them deliver unique user experiences to their customers. With the Dragon Drive platform, Nuance offers a deeply integrated hybrid solution that can be customized to become an OEM-branded smart automotive assistant which seamlessly integrates into the user’s connected ecosystem. Dragon Drive powers more than 200 million cars on the road today across more than 40 languages, creating conversational experiences for Toyota, Audi, BMW, Daimler, Fiat, Ford, GM, Hyundai, SAIC, and more.

Summary: Responsible for VB development and research, as well as AI related topics.

Responsibilities:

- VB research and development for various project’s requests as well as Nuance’s roadmap.

- Analysis of customer requirements for realizing VB solutions on features.
- Use existing tools technology or innovate new tech/tools to develop VB solutions to meet project’s need.
- Work with application development engineers and QA teams within the scope of one or more customer projects to realize the project deliverables.
- Develop demos and products on various HW/SW platforms like android, Linux, QNX devices.
- Development and maintenance of relevant processes for the use in production on key projects at Nuance.

Qualifications 

Required Skills:

- Solid VB/SR background and familiar with statistical modeling tools, such as HTK and Tensorflow etc.
- Experience on running accuracy experiments and systematically improving performance. Self-driven and diligent for solving real world problems.
- Proficiency in C/C++ programming skills and script programming (Bash/Python/Perl/Matlab).

- Native speaker of Chinese.
- Ability to manage tasks and deliverables for simultaneous projects.
- Ability to work well both independently and within a team.

Preferred Skills:

- Team work spirit.

- Fluent English communication, good communication skills.
- Accomplished coder – can realize research ideas effectively.

Education: MS/PhD degree in Computer Science, Engineering, Math or equivalent