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- Aufgaben: Dive into machine learning to make radar data understandable and impactful.
- Arbeitgeber: Join IMEC, a leader in AI/ML for wireless communications and networks.
- Mitarbeitervorteile: Publish your research, collaborate with experts, and innovate in a supportive environment.
- Warum dieser Job: Contribute to safer radar tech while enhancing human-machine collaboration in critical applications.
- Gewünschte Qualifikationen: Background in Engineering Technology, Engineering Science, Computer Science, or Informatics required.
- Andere Informationen: Reference code 2024-151 needed for application.
Das voraussichtliche Gehalt liegt zwischen 36000 - 60000 € pro Jahr.
Job Description
Understand and explain how machine learning techniques interpret radio frequency data for radar applications.
The rapid advancements in radar technology have led to radar innovations in various applications, including advanced driver assistance, autonomous vehicles, activity recognition, vital sign monitoring, and environmental monitoring. Novel machine learning techniques are often used to train radar solutions based on large radar datasets containing annotated time series from pulse radars, mmwave radars, or UWB radars. However, the black-box nature of traditional machine learning algorithms often hinders the interpretability of radar-based decision-making systems.
This PhD research aims to bridge the gap between radar applications and human understanding by investigating Explainable Artificial Intelligence (XAI) techniques tailored for radar data.
Expected Outcomes
- Developing Radar-Specific XAI Models: Design novel XAI models and techniques customized for radar data, considering the unique characteristics, such as high-dimensional data, temporal dependencies, and noise patterns.
- Interpretable Feature Extraction: Investigate methods for extracting interpretable features from radar data that capture relevant information while reducing complexity.
- Real-time Explainability: Explore real-time or near-real-time XAI solutions for radar applications to support decision-making in dynamic environments.
- Transferability to Various Radar Domains: Apply the developed XAI techniques to predict and enhance transferability to diverse radar applications, such as target tracking, object recognition, and vital sign monitoring, to assess their generalizability and adaptability.
The PhD dissertation will offer a comprehensive framework for integrating explainable AI techniques into radar applications, improving decision-making, reducing false alarms, and enhancing human-machine collaboration in radar-intensive environments. By shedding light on the decision-making process of radar systems, this research will pave the way for safer and more reliable applications of radar technology in critical domains.
The successful PhD candidate will be part of a large IMEC team working on the design and implementation of AI/ML for wireless communications and networks. This is a unique opportunity to develop innovative, multi-disciplinary technology and share future wireless networks. You will publish your research in top-tier journals and conferences.
Required Background
Engineering Technology, Engineering Science, Computer Science, Informatics or equivalent.
Type of Work
70% design/modeling/simulation, 20% experimental, and 10% literature.
Supervisors
Supervisor: Eli De Poorter
Co-supervisor: Steven Latre
Daily advisor: Adnan Shahid
The reference code for this position is 2024-151. Mention this reference code on your application form.
Enhancing Radio Frequency Radar Data Interpretability through Explainable AI Techniques Arbeitgeber: Eli De Poorter
Kontaktperson:
Eli De Poorter HR Team
StudySmarter Bewerbungstipps 🤫
So bekommst du den Job: Enhancing Radio Frequency Radar Data Interpretability through Explainable AI Techniques
✨Tip Number 1
Familiarize yourself with the latest advancements in Explainable AI (XAI) techniques, especially those tailored for radar data. Understanding how these techniques can enhance interpretability will give you a significant edge during discussions.
✨Tip Number 2
Engage with the radar technology community by attending relevant conferences or webinars. Networking with professionals in the field can provide insights and potentially valuable connections that may help you in your application process.
✨Tip Number 3
Consider working on personal projects or research that involve machine learning and radar data. Demonstrating hands-on experience with real-world applications will showcase your skills and commitment to the field.
✨Tip Number 4
Prepare to discuss how you would approach the challenges of integrating XAI into radar systems. Having a clear vision of your methodology will impress the selection committee and show your readiness for this PhD opportunity.
Diese Fähigkeiten machen dich zur top Bewerber*in für die Stelle: Enhancing Radio Frequency Radar Data Interpretability through Explainable AI Techniques
Tipps für deine Bewerbung 🫡
Understand the Research Focus: Make sure to thoroughly understand the research focus of the PhD position. Familiarize yourself with Explainable AI techniques and their application in radar technology, as this will help you tailor your application to highlight relevant skills and knowledge.
Highlight Relevant Experience: In your CV and cover letter, emphasize any experience you have in machine learning, radar technology, or related fields. Mention specific projects or coursework that demonstrate your ability to work with high-dimensional data and interpretability challenges.
Reference the Job Code: Don’t forget to include the reference code 2024-151 in your application form. This ensures that your application is correctly identified and processed by the hiring team.
Craft a Strong Motivation Letter: Write a compelling motivation letter that explains why you are interested in this PhD position. Discuss your passion for enhancing radar data interpretability and how your background aligns with the goals of the research. Be sure to convey your enthusiasm for contributing to the field.
Wie du dich auf ein Vorstellungsgespräch bei Eli De Poorter vorbereitest
✨Understand the Research Context
Make sure you have a solid grasp of the current advancements in radar technology and how they relate to machine learning. Familiarize yourself with the specific applications mentioned in the job description, such as autonomous vehicles and vital sign monitoring.
✨Showcase Your Technical Skills
Be prepared to discuss your experience with machine learning techniques, especially those related to explainable AI. Highlight any projects or research you've done that involved high-dimensional data or temporal dependencies, as these are crucial for this role.
✨Prepare Questions About XAI Techniques
Demonstrate your interest in the field by preparing insightful questions about explainable AI techniques tailored for radar data. This shows that you are not only knowledgeable but also genuinely interested in contributing to the research.
✨Highlight Collaboration Experience
Since you'll be part of a large team, emphasize any previous experiences where you collaborated on interdisciplinary projects. Discuss how you can contribute to team dynamics and share innovative ideas within a diverse group.
