Machine Learning for Signal Processing

The Department of Information and Communications Engineering is looking for outstanding

Postdoctoral Researchers in the area of Personalized Online Machine Learning for Wireless Networks

Are you an ambitious and talented researcher seeking to push the boundaries of machine learning and wireless communications? Do you want to be part of an innovative team working on cutting-edge projects that have real-world impact? If so, we invite you to apply for our prestigious three-year postdoctoral position at the Department of Information and Communications Engineering (DICE), Aalto University, Helsinki, Finland. 

Your role and goals

We are specifically looking for candidates with a deep passion for research and who demonstrate the potential to excel in various cutting-edge research areas related to statistical learning for emerging communications systems. These areas encompass meta-learning for heterogeneous systems, networked federated learning, privacy-preserving machine learning, Byzantine-resilient learning, and robust resource-efficient distributed optimization. As a postdoctoral researcher in PLEDGE, you will lead pioneering research at Aalto University's Department of Information and Communications Engineering. Your primary focus will be developing innovative algorithms and theories for reliable learning in resource-constrained networks with irregular and unstructured streaming data. You will be actively involved in creating decentralized personalized learning solutions in multi-agent wireless networks, such as the internet of things (IoT) and cyber-physical systems (CPS). Your work will be instrumental in advancing the digital transformation of industries and societies, contributing to solutions for various real-world challenges.

Your experience and ambitions

We seek a highly motivated and skilled researcher with a solid background in signal processing, statistical machine learning, optimization, wireless communications, or applied mathematics. Your expertise should be demonstrated through high-quality research publications, including at least one IEEE Transactions paper. Strong mathematical and programming skills, as well as proficiency in English, are essential. Your collaborative and proactive nature and excellent communication skills will be key to successfully contributing to the team. In summary, the ideal candidate has:

• A Ph.D. degree in Information and Communications Engineering, Electrical Engineering, Computer Science, or related fields.
• A strong research background with expertise in one or more of the following areas: signal processing, statistical machine learning, optimization, wireless communications, or applied mathematics.
• Proven track record of academic excellence demonstrated through high-quality research publications, including at least one IEEE Transactions paper.
• Solid programming skills and experience with relevant tools and libraries.
• A collaborative and proactive attitude with excellent communication skills
• Fluency in written and spoken English.

Don't miss out on this exciting opportunity – apply now! The best-qualified candidates will be invited for an interview.

Apply now

For more information about the position and application submission, please visit this page.

A fully funded PhD position in signal processing and machine learning is available in the DASN group at the School of Electrical and Electroinic Engineering, Nanyang Technological University, Singapore. 

Candidates must fulfil all admission requirements of the PhD programme:

1. Bachelor’s degree with minimum Honours (Distinction) or its equivalent;
2. Good English (TOEFL/IETLS) and technical (GRE/GATE) proficiency scores;
3. Supporting documents including candidate’s CV, research proposal and referee letters should also be provided

Research topics include innovative use of topological geometry, probability theory and graph signal processing techniques in understanding and developing graph neural networks and related deep learning architectures.

Interested candidates please contact Tay, Wee Peng.

Advanced driver assistance systems are key to enhancing road safety. One of the critical requirements for such systems is to reliably perceive the environment. The state-of-the-art sensors, however, are not ubiquitously deployed in driver assistance systems due to their high cost. On the other hand, simpler low-cost sensing solutions suffer from poor perception. 

In this project, you will address this gap by combining sensing capacities of the human driver and the driver assistance system, to develop sensing solutions that are both affordable and reliable. Your aim will be to develop signal processing algorithms and interfaces to incorporate driver in the sensing loop of automated driver assistance systems. Your work will leverage the unique cognitive abilities of humans controlling these systems to process complex signals and make informed real-time decisions. The project will lay the foundations for understanding how human interaction with signal processing systems impacts transparency and ethical considerations in deploying hybrid human-in-the-loop solutions.

In this project, you will be able to develop your skills in designing innovative human-in-the loop sensing solutions, rapid prototyping, and evaluation of your solutions in driving simulator experiments with human participants. You will work together with Dr. Nitin Myers from the Delft Center for Systems and Control, and Dr. Arkady Zgonnikov from the Department of Cognitive Robotics at TU Delft.

About the job

The Department of Electronic Systems (IES) has a vacancy for a Postdoctoral Researcher in statistical machine learning and distributed signal processing with applications in IoT. The successful candidate will be offered a three-year (3 years) appointment.

The position is linked to IoT@NTNU, a hub for IoT research at the Faculty of Information Technology and Electrical Engineering (IE), coordinated by IES. The project deals with big data, the internet of things (IoT), and artificial intelligence as critical enablers of IoT. In IoT, large volumes of data, often even personal data, are constantly gathered by numerous geographically dispersed sensors and devices, opening up a vast number of application areas that call for new approaches to data processing and inference. For example, concentrating the data at a central processing hub might be unfeasible due to constraints on the energy budgets of the data-collecting sensors/devices, the integrity of data holders, or the capacity of the communication channels. Thus, robust and privacy-preserving distributed or graph-based data processing over networks of machines/agents will be essential for performing data processing in future IoT-based applications. Examples of application areas of interest include but are not limited to, sensor fusion, health, smart cities, Industry 4.0, and environmental monitoring.

The postdoctoral research fellow will be affiliated with the IoT@NTNU and the Norwegian Open AI lab and collaborate with leading research scientists from international partner institutions. In addition, the hired candidate is to conduct research and possibly guide other students associated with the team. Therefore, preference will be given to candidates who can work independently and have a strong potential to build competence in advanced signal processing and machine learning methods with applications in IoT. 

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

Salary and conditions

As a Postdoctoral Fellow (code 1352) you are normally paid from gross NOK 563 500 per annum before tax, depending on qualifications and seniority. From the salary, 2% is deducted as a contribution to the Norwegian Public Service Pension Fund.

The period of employment is three (3) years.

The engagement is to be made in accordance with the regulations in force concerning State Employees and Civil Servants, and the acts relating to Control of the Export of Strategic Goods, Services and Technology. Candidates who by assessment of the application and attachment are seen to conflict with the criteria in the latter law will be prohibited from recruitment to NTNU.

About the application 

For more information and application submission, please visit the following link:

Postdoctoral Fellow in Signal Processing and Machine Learning for Emerging IoT Applications (241689) | NTNU - Norwegian University of Science and Technology (jobbnorge.no)

The research group Digital Mathematics at the Department of Mathematics and Data Science of the Vrije Universitieit Brussel in Belgium is looking for a postdoc in the mathematical foundations for data science (e.g. machine learning, image processing, ...) or post-quantum security. This position requires submitting a project to the Brussels' funding agency Innoviris. More details about the conditions and application procedure at https://we.vub.ac.be/en/dima-digital-mathematics/jobs

This PhD stipend is funded by the Pioneer Centre for Artificial Intelligence’s Collaboratory, Signals and Decoding. The Pioneer Centre for AI is located at the University of Copenhagen, with partners at Aarhus University, Aalborg University, The Technical University of Denmark, and the IT University of Copenhagen. There will be a cohort of PhD students starting during the fall of 2023 across the partner universities. PhD students at the Pioneer Centre for AI will have extraordinary access computing resources, to international researchers across many disciplines within computer sciences and other academic areas, as well as courses and events at the centre, and meaningful collaboration with industry, the public sector, and the start-up ecosystem.

Centre website: www.aicentre.dk

To date, most successful applications of deep learning in signals and decoding are based on supervised learning. However, supervised learning is contingent on the availability of labelled data, i.e., each sample has a semantic annotation. The need for labelled data is a serious limitation to applications at scale and complicates the maintenance of real-life supervised learning systems.

The typical situation is that unlabelled data is abundant, and this has given rise to paradigms such as semi-supervised and self-supervised learning (SSL). Both directions in SSL are based on combining large amounts of unlabelled data with limited labelled data. While semi-supervised learning invokes generative models to learn representations that support learning with few labels, self-supervised learning is based on supervised learning with a supervisory signal derived from the data itself.

The goal of this PhD study is to develop novel semi-supervised and self-supervised methods for modeling signals of various modalities (e.g., speech, audio, vision, text) and analyse the complexity of the developed models. The PhD student during the study is further provided with opportunities to do research at other units and the headquarter of the Pioneer Centre as well as abroad.

The PhD candidate is expected to have:

• A Master's degree (120 ECTS points) or a similar in Computer Science, Electronic Engineering, Computer Engineering, Applied Mathematics or equivalent.
• Knowledge with machine learning and deep learning.
• Hands-on experience with Python and deep learning frameworks.
• Experience with signal processing as a plus.
• Strong analytical and experimental skills.
• High-level of motivation and innovation.
• High-level of written and spoken English.

You may obtain further information from Professor Zheng-Hua Tan, Department of Electronic Systems, phone: +45 99 40 86 86, email: zt@es.aau.dk, concerning the scientific aspects of the stipend.

DEADLINE

02/04/2023

Apply online

Post-Doc position in multisensory signal processing and machine learning at Aalto University, Finland

We invite applications for a position as postdoctoral researcher to the project "Trustworthy array signal processing", based at Aalto University, Finland. The project will develop signal processing and machine learning theory and algorithms for large aperture sensor arrays that are resilient to unintentional and intentional interferences and can operate reliably in demanding radio environments and densely used radio spectrum. Applications are found, among others, in radar, multiantenna communications, and remote sensing systems.

The project is done in cooperation between Aalto University in Finland, and Linköping University (LiU), Sweden. The research is funded by WASP (Wallenberg AI, Autonomous Systems and Software Program). The program addresses research on artificial intelligence and autonomous systems acting in collaboration with humans, adapting to their environment through sensors, information and knowledge, and forming intelligent systems-of-systems.

Skills needed in the research:

- Strong background in wireless communications, signal processing, or reinforcement learning

- Very good publication record in top academic journals (at least 1 IEEE Transaction publication is required)

- Excellent analytical, technical and problem solving skills

- Very good organization and communication skills

Starting date: early 2023

We are looking for candidates interested in developing deep learning algorithms for brain-related 2D/3D MRI or microscopy images. Possible topics include image registration, segmentation or the automatic tracking/tracing of neurons in large 3D image data. We also welcome new project proposals related to brain/neuron image data analysis.

Experience in biomedical image analysis is a plus, but not a requirement. This position can be a great opportunity to apply knowledge and expertise from computer vision and/or image processing to new problems in the biomedical field.

Our webpage can be found here: https://bia.riken.jp We are looking forward to hearing from you

Postdoctoral Fellow, Research Scientist, and Instructor in Biomedical Optics and Medical Physics

We are looking for skilled and enthusiastic candidates to fill Postdoctoral Fellow, Research Scientist, and Instructor positions in the Biomedical Imaging and Radiation Technology Laboratory(BIRTLab) at Department of Radiation Oncology in UT Southwestern Medical Center. Our mission is to innovate, develop, and apply biomedical technology to empower cancer research.

Successful candidates will be joining our team to work either one of the following projects:

(1).       Establishing an ultra-sensitive optical imaging integrated with X-ray cone beam CT(CBCT)/MRI imaging for in vivo tumor and immune cell tracking to facilitate cancer therapy development. The research involves imaging reconstruction, and the development of camera-lens imaging system or single pixel imaging.

(2).       Developing optical tomography-guided system for pre-clinical radiation therapy research; specifically, we will develop a fluorescence, bioluminescence, and diffuse optical tomography system to localize tumors in vivo, guide irradiation, and quantify treatment response.

(3).       Establishing molecular image-guided system for ultra-high-dose rate (FLASH) radiation therapy as a new cancer treatment paradigm; the research involves numerical oxygen transport modeling, Monte-Carlo simulation, optical imaging and radiation physics to assess the efficacy of FLASH therapy.

The projects are multi-disciplinary and integrate engineering, algorithm development, optics, radiation physics, biology, and industrial components. Success completion of the projects will significantly advance image-guided systems and radiation technology to improve cancer treatment.

BIRTLab provides an outstanding environment to grow candidates toward successful careers.

• PI Dr. Wang works tirelessly with candidates to ensure they meet their career goals. Through attentive guidance, he encourages members to think creatively and develop their own research projects. All activities are supported by extramural funding through the NIH and Texas CPRIT.
• Successful members are also eligible for basic clinical medical physics training and a tuition fee waiver to enroll in a certificate program with CAMPEP-accredited courses, which covers medical physics didactic elements for people who enter the medical physics profession through an alternative pathway.

Multi-disciplinary projects, a strong research environment, and the medical physics pathway together provide a unique opportunity to prepare the candidate for careers in academia and industry, or to become a professional medical physicist in the U.S.

Candidates with established experience in numerical algorithm development, biomedical optics, or engineering system design and development are desired. Candidates who hold degrees in mathematics, physics, biomedical engineering, optics, computer science and engineering are encouraged to apply. Further details about the BIRTLab and projects can be found at https://www.utsouthwestern.edu/labs/birt/

Position and compensation are based on candidates’ experience and NIH scale with highly competitive benefits. UT Southwestern Medical Center is in Dallas, Texas. Dallas is the fourth-largest metropolitan area in the US with fast growing industrial sectors and job opportunities. Interested candidates should send a statement of interest, CV, and the contact of 3 references to:

Ken Kang-Hsin Wang, Ph.D., DABR

Associate Professor

CPRIT Scholar in Cancer Research

Division of Medical Physics and Engineering

Department of Radiation Oncology

UT Southwestern Medical Center

Kang-Hsin.Wang@utsouthwestern.edu