Machine Learning for Signal Processing

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1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
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MLSP

Professor

ELLIS Institute Finland, a newly established world-class research hub in AI and machine learning, invites applications for Principal Investigator (PI) positions and a joint appointment as assistant professor at School of Electrical Engineering, Aalto university, Finland.The topic area is broadly Intelligent Systems. Detailed call can be found from:
https://www.ellisinstitute.fi/PI-recruit

Assistant/Associate/Full Professor in Computing Science (Fundamental and Applied AI)

Tampere University has several professor positions open related to AI and its applications, covering various areas of signal processing. The positions include a quite substantial starting package, covering funding for multiple research group members. Strong researchers are encouraged to apply! The deadline for applications is 9 March 2025. For more information about the positions, please visit this page.

PhD Stipend in Multimodal Reasoning with Large Language Models

Large language models(LLMs) have demonstrated increasingly powerful capabilities for reasoning tasks, especially in text. The project aims to explore and advance these capabilities in reasoning across multiple data modalities, including but not limited to text, speech and audio. The integration of multiple modalities can lead to more robust and general systems capable of understading and reasoning about the world in a more human-like manner. The project will involve fine-tuning pre-trained models and developing self-supervised learning techniques to adapt LLMs for multimodal tasks.

Postdoctoral Fellow in Information Theory/Machine Learning

Do you want to dive into the exciting field of distributed machine learning with a special focus on privacy/security? This research topic is going to shake up how we understand and apply machine learning, with the aim of creating safer and more private solutions in an increasingly digitized world. Successful applicants will have the opportunity to explore and contribute to groundbreaking research questions.

Professor

The School of Engineering & Applied Science (SEAS) at the University of Virginia (UVa) seeks candidates for a tenure-eligible or tenured position in the Department of Electrical and Computer Engineering. The primary responsibilities for this position include research, teaching, and service to the department, university, and professional community. The appointment rank and compensation will be commensurate with experience and qualifications. 

Postdoctoral researcher

Applications are invited for postdoctoral researcher positions in the general area of optimization and learning of network systems. Competitive financial supports will be provided.

Candidates with a clear interest in the general area of network systems are encouraged to apply.

Specific areas of research include:

Tenure-track professor, Signal Processing and Machine Learning

The Signal Processing Research Centre of Tampere University is looking for a tenure track-professor (assistant/associate/full) to join its team of world-class experts. Please see the full description and apply here.

 

PhD Opportunities in AI for Digital Media Inclusion (Deadline 30 May 2024)

** PhD Opportunities in Centre for Doctoral Training in AI for Digital Media Inclusion
** Surrey Institute for People-Centred AI at the University of Surrey, UK, and
** StoryFutures at Royal Holloway University of London, UK

** Apply by 30 May 2024, for PhD cohort starting October 2024

URL: https://www.surrey.ac.uk/artificial-intelligence/cdt

Postdoc in Self-Supervised Learning for Decoding of Complex Signals

We are excited to announce a two-year postdoc position in self-supervised and weakly-supervised learning for signals, e.g. speech, audio, text, and images. While the success of deep learning largely relies on the presence of substantial amounts of labeled data, the prevailing reality often entails the abundance of unlabeled or inadequately labeled data. This project focuses on the development of weakly-supervised and self-supervised learning methods to harness these data resources and gain deeper insights into the underlying mechanisms of these methods.

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