Postdoctoral Researcher/Doctoral Student/Project Researcher (Imaging/Sensing/Automatic control), 1-3 positions

The Signal and Image Restoration Group is part of the Laboratory of Signal Processing at Tampere University of Technology. The group research is dedicated to the characterization, transformation, and filtering of noise and other degradations for a variety of consumer, medical, and scientific imaging devices. The group develops theoretically grounded models, methods, and regularization priors for unsupervised processing of data from a diverse range of sensors, including direct, inverse, as well as computational imaging systems, with the ultimate goal of substantially improving the sensing/imaging quality and extending the applicability and efficiency of these devices. It has a strong scientific profile and is involved in national and international projects with both academic and industrial partners.

Job description:    

The Signal and Image Restoration Group is currently looking for motivated and talented postdoctoral and doctoral-student level researchers to contribute to ongoing research projects. The main problems to be investigated include image sensing and restoration at extremely low energy levels (with application to inverse problems in physics and medicine), and adaptive control of ultrafast broadband laser sources.

The positions are strongly research focused. Activities include conducting empirical research, theoretical analysis, algorithm design, software development and validation, reading and writing scientific articles, presentation of the research results at seminars and conferences in Finland and abroad, acquiring (or assisting in acquiring) further funding.

Candidates hired for Doctoral Student positions will work towards completion of a PhD degree under the supervision of the senior members of the research group.

Requirements:    

Candidates should hold a master or doctoral degree in image processing, computer science and/or engineering, data science, applied mathematics, or related areas.

Candidates are also expected to have good skills in scientific programming (preferably Matlab, Python, and/or C), proficiency in English, both written and spoken.

The following qualities are appreciated:

 * a strong background in linear algebra, statistical estimation, machine learning, and/or numerical optimization;

 * experience working with real data;

 * experience working with sensors and control systems.

Candidates at the postdoctoral level must have a demonstrated ability to carry out independent research in at least one of the following fields: signal and image processing, machines learning, multivariate statistics.

Information and application instructions:
https://careers.fi/tty/careers.cgi?action=view&job_id=1353&lang=uk

Salary: GBP 30,688 to GBP 38,833 per annum
Closing Date: 1 May 2018 (23:00 BST)
https://jobs.surrey.ac.uk/021518

Applications are invited for a Research Fellow in Machine Listening to work full-time on an EPSRC-funded project "Making Sense of Sounds", to start as soon as possible, for 9.75 months until 13 March 2019. This project is investigating how to make sense from sound data, focussing on how to allow people to search, browse and interact with sounds. The candidate will be responsible for investigating and developing machine learning methods for analysis of everyday sounds, leading to new representations to support search, retrieval and interaction with sound.

The successful applicant is expected to have a PhD or equivalent in electronic engineering, computer science or a related subject, and is expected to have significant research experience in audio signal processing and machine learning. Research experience in one or more of the following is desirable: deep learning; blind source separation, blind de-reverberation, sparse and/or non-negative representations, audio feature extraction.

The project is being led by Prof Mark Plumbley in the Centre for Vision Speech and Signal Processing (CVSSP) at the University of Surrey, in collaboration with the Digital World Research Centre (DWRC) at Surrey, and the University of Salford. The postholder will be based in CVSSP and work under the direction of Prof Plumbley and Co-Investigators Dr Wenwu Wang and Dr Philip Jackson. For more about the project see: http://cvssp.org/projects/making_sense_of_sounds/

CVSSP is an International Centre of Excellence for research in Audio-Visual Machine Perception, with 125 researchers, a grant portfolio of £20M. The Centre has state-of-the-art acoustic capture and analysis facilities enabling research into audio source separation, music transcription and spatial audio. Audio-visual compute includes 700 cores and a 50GPU machine learning cluster with 500TB of online storage.

Informal enquires are welcome, to: Prof Mark Plumbley (m.plumbley@surrey.ac.uk), Dr Wenwu Wang (w.wang@surrey.ac.uk), or Dr Philip Jackson (p.jackson@surrey.ac.uk).

For more information and to apply online, please visit:

https://jobs.surrey.ac.uk/021518

We acknowledge, understand and embrace diversity.

The Faculty of Information Technology and Electrical Engineering, at the Norwegian University of Science and Technology (NTNU) has two (2) PhD Research Fellow vacancies at the Department of Electronic Systems (IES).

Each of the PhD positions is for up to 4 years with 25% work assignments for NTNU IES.

Project Description

The pervasion of the Internet of Things (IoT) which connects numerous sensors, actuators, appliances, vehicles etc., will have a strong impact on the evolution of smarter and greener cities as well as on environmental monitoring. A basic tenet underlying all key functionalities of the IoT is situational awareness, i.e., the ability to capture events and derive accurate, critical information to facilitate decision making for timely actions in a heterogeneous and highly dynamic environment. The scale and largely interconnected nature of IoT, together with the stringent requirements on low energy and limited hardware, pose severe challenges to security. The network of IoT is vulnerable to various forms of man-made intrusion or natural disruption. For example, an adversary may gain full control over a subset of the devices, and maliciously alter the reported measurements. This calls for an intelligent infrastructure that is autonomous, dependable, and resilient to natural or man-made disturbances. To ensure that the IoTs operate effectively, we need to take a holistic approach to designing secure sensor networks with resource-efficient functional algorithms starting with sensing, followed by data processing and communication to ensure reliable decision making for long long-lasting, secure, and dependable services.

The PhD projects will be around developing and analyzing new efficient distributed detection, estimation and (machine) learning schemes to improve data quality and security of the physical-layer signals in IoT. The aim is to go beyond the state-of-the-art solutions and take a holistic approach encompassing intelligent sensors, smart inference, and secure two-way communication among all the devices in the network.

The application

More information and for the application submission, please click this link. Please submit an application letter describing your motivation, relevant experience, skills and qualifications, and a brief research vision for the position (maximum 2 pages) along with a CV, publication list, letters of reference and proof of fluency in the English language (if applicable) and certificates from both Bachelor and Master degrees.

Applicants are kindly requested to send a diploma supplement or a similar document, which describes in detail the study and grading system and the rights for further studies associated with the obtained degree.
Incomplete applications will not be taken into consideration

Formalities

The appointment is made in accordance with the regulations of employment for PhD candidates issued by the Ministry of Education and Research, with relevant parts of the additional guidelines for appointment as a PhD candidate at NTNU. Applicants must participate in an organized PhD programme of study during their period of employment. The candidate appointed must comply with the regulations for employees in the public sector. In addition, a contract will be signed regarding the period of employment.

Applicants must be qualified for admission to a PhD study program at NTNU. See http://www.ntnu.no/ie/forskning/phd for information about PhD studies at NTNU.

We can offer

• an informal and friendly workplace with dedicated colleagues
• academic challenges in a cross-disciplinary team
• attractive schemes for home loans, insurance and pensions through the Norwegian Public Service Pension Fund

Depending on qualifications and academic background, PhD Candidates (in code 1017) at the Faculty of Information Technology and Electrical Engineering will be remunerated at a minimum of NOK 436 500 per year before tax. Normal wage level is NOK 436 500 of which 2% is deducted for the Norwegian Public Service Pension Fund. The appointment is subject to the conditions in effect at any time for employees in the public sector.

The Faculty of Information Technology and Electrical Engineering, at the Norwegian University of Science and Technology (NTNU) has one Postdoctoral Research vacancy at the Department of Electronic Systems (IES). The postdoctoral position is for two (2) years.

Project Description

In Internet-of-things (IoT), as sensing and estimation becomes more distributed, sensors/estimators will need to communicate with each other and other entities in the network to obtain an accurate picture of the system being observed. In a distributed IoT/wireless sensor network, the sensor nodes share their observations with the neighboring nodes and cooperate to enhance the accuracy of the inference. As the number of sensors increases, the amount of data that will be stored, processed and communicated increases dramatically so that system throughput and latency will not be adequate to ensure reliable and secure operation. In this project we are particularly interested in the development of new methods and algorithms that enable frugal usage of energy by the sensors, that can effectively convert the data collected by myriads of sensors into actionable intelligence, and that are robust and resilient to malfunctioning sensors or malicious disturbance.

The candidate will conduct research together with personnel in the group of Prof. Stefan Werner, as well as researchers connected to the NTNU Internet-of-things lab. Preference will be given to candidates who can work independently and have a strong potential to build competence and guide students within the following research areas:

1. Decentralized statistical learning for energy-efficient sensor networks in IoT
2. Graph signal processing in IoT
3. Distributed optimization with applications to IoT/wireless sensor networks

We search for candidates with keen interests in these tasks, and those with the best qualifications will be invited for an interview.

Qualifications

The candidate is expected to be able to work independently, have a solid mathematical background and a PhD in statistical signal processing, applied mathematics, statistics or machine learning, wireless sensor networks, electrical engineering, or related field. Good command in English language (spoken and written) is a prerequisite. An eligible candidate should have submitted the PhD thesis for assessment by the application deadline.

The application

More information and for the application submission, please click this link. The following documents need to be attached in the application:

• A brief research statement, describing the candidate’s research interests and plans, and publication plan (maximum 3 pages in total)
• CV as PDF including a full list of publications with bibliographical references Indicate the most important publications that are relevant for the evaluation of the applicant’s qualifications (maximum 10 publications).
• Testimonials and certificates
• Other documents which the applicant would find relevant

Incomplete applications will not be taken into consideration.

We can offer

• an informal and friendly workplace with dedicated colleagues
• academic challenges in a cross-disciplinary team
• attractive schemes for home loans, insurance and pensions through the Norwegian Public Service Pension Fund

For further information about the position, contact Professor Stefan Werner, email: stefan.werner@ntnu.no

Depending on qualifications and academic background, postdoctoral fellows (1352) will be remunerated at a minimum of NOK 490 500 per year before tax and the position as. There will be a 2 % deduction to the Norwegian Public Service Pension Fund from gross wage. The appointment is subject to the conditions in effect at any time for employees in the public sector.

Tenure-track Assistant Professor in Information Engineering

The Biomedical Imaging & Graphics (BIG) lab at the University of Saskatchewan, Canada, led by Dr. Ian Stavness, is hiring a fully-funded PhD Student or Post-doc in Computer Science / Electrical Engineering / Biomedical Engineering to work on deep learning in speech and neural signal processing. We plan combine powerful computer simulations of vocal tract biomechanics with neural and acoustic signals of speech production captured with state-of-the-art magnetoencephalography (MEG) measurement. The PhD/Post-doc will contribute to ArtiSynth (http://www.artisynth.org) an open source toolkit used by researchers around the world to create 3D models of tissue mechanics and fluid dynamics, as well as the development of deep learning architectures for brain/speech data.

We are searching for a bright and enthusiastic individual to join our team and make an expressive, real-time computer brain-speech interface a reality. The ideal candidate will have strong computer programming skills, experience with modern machine learning methods and a keen interest in biomechanics / biomedical / speech / brain research. Prior experience with deep learning, finite-element analysis, digital signal processing, biomechanics, software engineering, robotics, and/or controls is also desirable. The candidate will have opportunities to do an industrial internship with our partner on MEG sensing, as well as an academic internship with our collaborators at the University of British Columbia.

Interested applicants should send a letter indicating their interest and experience, a CV, and transcripts to ian.stavness@usask.ca (please use subject line “Deep Learning Brain position”)

The Lab focuses on sampling, modeling and processing of continuous-time and discrete-time signals and on new design paradigms in which sampling and processing are designed jointly in order to exploit signal properties already in the sampling stage. This approach has the potential to drastically reduce the sampling and processing rates well below the Nyquist rate, typically considered as the ultimate limit for analog to digital conversion. The laboratory facilitates the transition from pure theoretical research to the development, design and implementation of prototype systems ( DOA, MIMO, SAR and more.. ) in areas ranging from bio imaging trough communications, laser optics, cognitive radio, radar systems and graph signal processing.

We are looking for thought leaders, M.Sc. & Ph.D. students who can develop new areas and applications in RADAR and remote sensing. The applicant should be very comfortable with Radar hardware design and will lead research activity. The balance of work between theory and hardware will vary on project-basis, and a successful candidate should be proficient in both aspects. 

Required Background:

• Signal and Systems
• Mavlas
• Random Signals

The Biomedical Imaging & Graphics (BIG) lab at the University of Saskatchewan, Canada, led by Dr. Ian Stavness, is hiring a fully-funded PhD Student or Post-doc in Computer Science / Electrical Engineering / Biomedical Engineering to work on deep learning in speech and neural signal processing. We plan combine powerful computer simulations of vocal tract biomechanics with neural and acoustic signals of speech production captured with state-of-the-art magnetoencephalography (MEG) measurement. The PhD/Post-doc will contribute to ArtiSynth (http://www.artisynth.org) an open source toolkit used by researchers around the world to create 3D models of tissue mechanics and fluid dynamics, as well as the development of deep learning architectures for brain/speech data.

We are searching for a bright and enthusiastic individual to join our team and make an expressive, real-time computer brain-speech interface a reality. The ideal candidate will have strong computer programming skills, experience with modern machine learning methods and a keen interest in biomechanics / biomedical / speech / brain research. Prior experience with deep learning, finite-element analysis, digital signal processing, biomechanics, software engineering, robotics, and/or controls is also desirable. The candidate will have opportunities to do an industrial internship with our partner on MEG sensing, as well as an academic internship with our collaborators at the University of British Columbia.

Interested applicants should send a letter indicating their interest and experience, a CV, and transcripts to ian.stavness@usask.ca (please use subject line “Deep Learning Brain position”).