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SP1582: Source & Channel Coding
MERL is seeking a highly motivated, qualified individual to join our internship program of research on applied coding for data science. The ideal candidate is expected to possess an excellent background in channel coding, source coding, information theory, coding theory, coded modulation design, signal processing, deep learning, quantum computing, and molecular computing. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.
- Research Areas: Communications, Machine Learning, Signal Processing
- Host: Toshi Koike-Akino
- Apply Now
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SP1475: Advanced Signal Processing for Metasurface
MERL is seeking a highly motivated, qualified intern to join an internship program. The ideal candidate will be expected to carry out research on Advanced Signal Processing for Metasurface. The candidate is expected to develop innovative signal processing for metasurface aided various applications. Candidates should have strong knowledge about electromagnetic field analysis for metasurface, passive beamforming, interference mitigation, and channel estimation. Proficient programming skills with Python, MATLAB, and C++, and strong mathematical analysis will be additional assets to this position. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply. The expected duration of the internship is 3-6 months, with a flexible start date in 2020. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.
- Research Areas: Applied Physics, Communications, Signal Processing
- Host: K.J. Kim
- Apply Now
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SP1517: AI-based spectrum management for 5G wireless networks and beyond
MERL is seeking a highly motivated, qualified intern to join a thirteen weeks internship program. The ideal candidate will be expected to carry out research on emerging 5G wireless networks and beyond for industrial applications. The candidate is expected to develop innovative spectrum-based traffic recognition and optimal scheduling for local spectrum access. Candidates should have strong knowledge about 5G networks, spectrum management, cognitive radio, and neural network. Proficient programming skills with MATLAB, C++, Python (Pytorch), experience with ns-3 simulator, and strong mathematical analysis will be additional assets to this position. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.
- Research Areas: Artificial Intelligence, Communications, Machine Learning, Signal Processing
- Host: K.J. Kim
- Apply Now
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SP1522: AI Security for Cyber Physical Systems
MERL is seeking a highly motivated, qualified intern to join a thirteen weeks internship program. The ideal candidate will be expected to carry out research on AI security for various cyber physical systems. The candidate is expected to develop innovative AI technologies to increase cyber security. Candidates should have strong knowledge about neural network and learning techniques, such as feature extraction, machine learning, explainable learning, and distributed learning. Proficient programming skills with Pytorch, and strong mathematical analysis will be additional assets to this position. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.
- Research Areas: Artificial Intelligence, Communications, Machine Learning, Signal Processing
- Host: K.J. Kim
- Apply Now
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SP1516: Machine Learning for Optical Communications
MERL is seeking an intern to work on machine learning for coherent optical transmission systems. The ideal candidate would be an experienced PhD student or post-graduate researcher working in coherent optical communications. The candidate should have a detailed knowledge of optical communications, with some experience in machine learning, probabilistic shaping, coded modulation or ultra-wideband optical transmission systems preferred. Strong programming skills in MATLAB or Python are essential. Experience of working in an optical lab environment is a required. Duration is 3 to 6 months. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.
- Research Areas: Communications, Signal Processing
- Host: David Millar
- Apply Now
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SP1537: Machine Learning for Wireless Communications
MERL is seeking an intern to work on machine learning for wireless communication systems. The ideal candidate would be an experienced PhD student or post-graduate researcher working in wireless communications with a focus on machine learning methods. The candidate should have a detailed knowledge of wireless communications, with some experience in machine learning, MIMO, and/or channel equalization preferred. Strong programming skills in Python and machine learning frameworks are essential. The expected duration of the internship is 3-6 months with flexible start date and length. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.
- Research Areas: Artificial Intelligence, Communications, Machine Learning
- Host: Ye Wang
- Apply Now
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SP1506: Learning-based Wireless Sensing
The Signal Processing (SP) group at MERL is seeking a highly motivated intern to conduct fundamental research in learning-based wireless sensing using communication signals (such as WiFi, Bluetooth, 5G) and other RF signals (such as FMCW). Previous experience in occupancy sensing, people counting, localization, device-free pose/gesture recognition, and skeleton tracking with deep learning is highly preferred. Familiarity with IEEE 802.11 (g/n/ac/ad/ay)standards is a plus. The intern will collaborate with a small group of MERL researchers to develop novel algorithms, design experiments using MERL in-house testbed, and prepare results for publication. Senior Ph.D. students with research focuses on wireless communications, machine learning, signal processing, optimization, applied mathematics, or related areas are encouraged to apply. The expected duration of the internship is 3 months with a flexible start date. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.
- Research Areas: Artificial Intelligence, Communications, Computational Sensing, Dynamical Systems, Machine Learning, Robotics, Signal Processing
- Host: Perry Wang
- Apply Now
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SP1512: Mutual Interference Mitigation
The Signal Processing (SP) group at MERL is seeking a highly motivated intern to conduct fundamental research in mutual interference mitigation for automotive radar. Previous experience in waveform design, radar detection under interference, joint communication and sensing, interference mitigation, and deep learning for radar is highly preferred. Knowledge about automotive radar schemes (MIMO and waveform modulation, e.g., FMCW, PMCW, and OFDM) is a plus. The intern will collaborate with a small group of MERL researchers to develop novel algorithms, design experiments using MERL in-house testbed, and prepare results for patents and publication. Senior Ph.D. students with research focuses on signal processing, machine learning, optimization, applied mathematics, or related areas are encouraged to apply. The expected duration of the internship is 3 months with a flexible start date.
- Research Areas: Artificial Intelligence, Communications, Computational Sensing, Data Analytics, Dynamical Systems, Machine Learning, Optimization, Signal Processing
- Host: Perry Wang
- Apply Now