עדיין מחפשים עבודה במנועי חיפוש? הגיע הזמן להשתדרג!
במקום לעבור לבד על אלפי מודעות, Jobify מנתחת את קורות החיים שלך ומציגה לך רק משרות שבאמת מתאימות לך.
מעל 80,000 משרות • 4,000 חדשות ביום
חינם. בלי פרסומות. בלי אותיות קטנות.
מלאה
25,000-35,000 ₪
We are seeking a Machine Learning expert to join our wireless FW software group and explore innovative applications of ML algorithms in embedded wireless systems and protocols. This role focuses on cutting-edge research to integrate machine learning into wireless firmware architectures as well as protocol design, validation, and performance optimization. The position emphasizes experimental research, algorithm development, and proof-of-concept implementations in the wireless FW domain. This role offers the opportunity to develop novel solutions that bridge the gap between theoretical machine learning capabilities and practical wireless system implementations.
Responsibilities:
Research & Development: Design, develop, and evaluate machine learning algorithms optimized for real-time wireless communication systems operating under strict real-time, power and computational constraints.
Protocol Innovation: Create novel wireless protocols that intelligently incorporate ML techniques for enhanced performance, efficiency, and adaptability.
Algorithm Optimization: Adapt existing wireless algorithms (e.g. Link Quality, QoS, Scheduling etc) by integrating ML approaches while maintaining real-time performance requirements.
Data Collection & Analysis: Gather, analyze, and curate statistics from existing wireless devices and networks to enable ML model training and validation. Design and implement data collection frameworks for various wireless environments and use cases.
System Integration: Develop lightweight ML models suitable for deployment on resource-constrained wireless devices, including edge computing scenarios.
Performance Analysis: Conduct comprehensive performance evaluations through simulation, theoretical analysis, and experimental validation.
Research Publication: Publish findings in top-tier conferences and journals, and present research at industry conferences.
Collaboration: Work closely with hardware engineers, system architects, and product teams to ensure research translates into practical solutions.
Technology Scouting: Stay current with emerging trends in ML, wireless communications, and edge computing technologies.
Responsibilities:
Research & Development: Design, develop, and evaluate machine learning algorithms optimized for real-time wireless communication systems operating under strict real-time, power and computational constraints.
Protocol Innovation: Create novel wireless protocols that intelligently incorporate ML techniques for enhanced performance, efficiency, and adaptability.
Algorithm Optimization: Adapt existing wireless algorithms (e.g. Link Quality, QoS, Scheduling etc) by integrating ML approaches while maintaining real-time performance requirements.
Data Collection & Analysis: Gather, analyze, and curate statistics from existing wireless devices and networks to enable ML model training and validation. Design and implement data collection frameworks for various wireless environments and use cases.
System Integration: Develop lightweight ML models suitable for deployment on resource-constrained wireless devices, including edge computing scenarios.
Performance Analysis: Conduct comprehensive performance evaluations through simulation, theoretical analysis, and experimental validation.
Research Publication: Publish findings in top-tier conferences and journals, and present research at industry conferences.
Collaboration: Work closely with hardware engineers, system architects, and product teams to ensure research translates into practical solutions.
Technology Scouting: Stay current with emerging trends in ML, wireless communications, and edge computing technologies.
Requirements:
Minimum Qualifications
Minimum: Master's degree (MSc) in one of the following fields: Computer Science or Mathematics with specialization in Machine Learning, Signal Processing and Telecommunications Engineering.
Real-time Systems: Experience in real-time computing constraints and low-latency system design.
Machine Learning: Deep understanding of ML algorithms, particularly those suitable for real-time applications (online learning, federated learning, reinforcement learning, neural networks).
Wireless Communications: Strong foundation in wireless communication principles, protocols.
Programming: Proficiency in Embedded C, Python, MATLAB, C++, and ML frameworks.
Optimization: Experience with convex optimization, resource allocation algorithms, and constraint satisfaction problems.
Research Experience: Demonstrated track record of independent research through publications, patents, or significant project contributions.
Knowledge of edge computing and distributed ML systems is advantageous.
Personal Attributes: Self-Motivated: Ability to work independently, set research priorities, and drive projects from conception to completion.
Analytical Thinking: Strong problem-solving skills with ability to tackle complex, multi-disciplinary challenges.
Innovation-Oriented: Creative approach to research with ability to think outside conventional boundaries.
Communication Skills: Excellent written and verbal communication skills for technical documentation and presentations.
Collaborative Spirit: Ability to work effectively in cross-functional teams while maintaining independent research focus.
Preferred Qualifications
Ph.D. in related field.
Minimum Qualifications
Minimum: Master's degree (MSc) in one of the following fields: Computer Science or Mathematics with specialization in Machine Learning, Signal Processing and Telecommunications Engineering.
Real-time Systems: Experience in real-time computing constraints and low-latency system design.
Machine Learning: Deep understanding of ML algorithms, particularly those suitable for real-time applications (online learning, federated learning, reinforcement learning, neural networks).
Wireless Communications: Strong foundation in wireless communication principles, protocols.
Programming: Proficiency in Embedded C, Python, MATLAB, C++, and ML frameworks.
Optimization: Experience with convex optimization, resource allocation algorithms, and constraint satisfaction problems.
Research Experience: Demonstrated track record of independent research through publications, patents, or significant project contributions.
Knowledge of edge computing and distributed ML systems is advantageous.
Personal Attributes: Self-Motivated: Ability to work independently, set research priorities, and drive projects from conception to completion.
Analytical Thinking: Strong problem-solving skills with ability to tackle complex, multi-disciplinary challenges.
Innovation-Oriented: Creative approach to research with ability to think outside conventional boundaries.
Communication Skills: Excellent written and verbal communication skills for technical documentation and presentations.
Collaborative Spirit: Ability to work effectively in cross-functional teams while maintaining independent research focus.
Preferred Qualifications
Ph.D. in related field.
This position is open to all candidates.
במקום לעבור לבד על אלפי מודעות, Jobify מנתחת את קורות החיים שלך ומציגה לך רק משרות שבאמת מתאימות לך.
מעל 80,000 משרות • 4,000 חדשות ביום
חינם. בלי פרסומות. בלי אותיות קטנות.
פתח תקווה
ערב
7 חודשים
סבסוד
