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Fellow-Mentor-Advisor (FMA) Fellowships

The Qualcomm Fellow-Mentor-Advisor Fellowship is a fellowship program for outstanding UCSD Jacobs School of Engineering doctoral students nominated by their Faculty Advisors.

The Program brings together teams which include an engineering Ph.D. candidate, his/her faculty advisor and an engineering mentor from Qualcomm. The goal is to foster in-depth connections between the Jacobs School faculty and Qualcomm engineers while enhancing the education of doctoral students. Qualcomm FMA fellowships are awarded annually (a minimum of 4).

Fellowship Summary

  • Total monetary value of the award is $75,000.
  • Student's tuition, fees and stipend for up to 12 months.
  • Discretionary funding to the Faculty Advisor's research program related to the research plan of the fellow; includes conference travel funds for fellow.
  • Fellows will receive guidance from an experienced Qualcomm Mentor.
  • Payment of the fellowship award, as described above, will be made directly to the university, and is not transferable to another academic institution or department.

Eligibility Criteria

  • Applicants for the Qualcomm FMA must be nominated by their faculty advisor.
  • Student must be enrolled as a doctoral student in good standing within one of the six academic departments of the UCSD Jacobs School of Engineering.
  • Students must have completed at least one year of study at UCSD in their doctoral program.
  • Student should be working in a research topic of interest to Qualcomm's future business development.

Identifying a Qualcomm Mentor

  • Qualcomm Mentors are Qualcomm employees whose expertise is appropriate to the research project and who will be an integral member of the research team.
  • Qualcomm Mentors may be individuals with whom the Faculty Advisor already works.
  • In the case that a mentor is unknown, please indicate on Interest Statement. Qualcomm will circulate the Interest Statement to see if there is an appropriate engineering leader at Qualcomm who may want to serve as a mentor on the project.
  • If a mentor is identified, the Qualcomm Mentor will contact the Faculty Advisor so they can confirm mutual interests and possibly invite the faculty member to present a seminar at Qualcomm before the final application is submitted.
  • The FMA Mentorship is for one year, at minimum.

How to Apply

Step One: Interest Statement

  • FMA interest statement can be submitted anytime.
  • The interest statement submission should include a one-paragraph description of the proposed student research project, and indication of whether a Qualcomm Mentor is identified or not.
  • Please allow a minimum of one month from submission for Qualcomm's review.

Step Two: Application

  • If your interest statement is selected, a Qualcomm representative will contact you to proceed and submit an application.
  • The Faculty Advisor should include the following information with their submission:
    • Student's research project description (1-2 pages developed in consultation with Qualcomm Mentor)
    • Student Nominee's CV
    • Faculty Advisor's short CV (1-2 page)
  • Qualcomm will review the application and make the final award selections.

Technical Areas of Interest

Qualcomm has provided some technical areas of interest. Other technical area proposals not indicated below can be submitted, as this list is not all inclusive.

  • Ultra-low (uW) power embedded platform for edge computing (ULP architectures and designs, HW accelerators, power generation and management, novel memories, security)
  • Novel materials and heterogeneous integration (2D semiconductors, GaAs, GaN, etc.)
  • CMOS (3D IC, thermal-aware designs, circuits, advanced packaging techniques, etc.)
  • RF / analog ASICs and architectures (Sub-6GHz 5G power amplifiers, mmWave RFIC for 5G NR, adaptive RF signal processing algorithms, etc.)
  • Advanced antenna (millimeter-wave and phase-array antennas), novel antenna materials, structures and implementations
  • Power Management ASICs (wide bandwidth SMPS, wide bandwidth envelope tracker, embedded regulation)
  • Ultra-reliable and low latency communications
  • Wide-area wireless networks using high-frequency and mmWave spectrum
  • Massive MIMO, network MIMO, and coordinated multipoint processing
  • Wireless systems for unlicensed/shared spectrum
  • Low energy networks (Bluetooth LE, 802.15.4, Zigbee, Wi-Fi, etc.)
  • Advanced low power HW/FW/SW modem implementation approaches
  • Advanced sensors and sensor fusion
  • Imaging radar
  • Computer vision for autonomy
  • Sensor fusion with deep learning
  • Behavior planning with uncertainty
  • Natural language processing
  • Computer vision
  • Reinforcement and continual learning
  • On-device training
  • Intermediate representation for machine learning workloads/compilers
  • Transfer learning and Knowledge distillation
  • Novel compute architectures for ML tasks, e.g. in-memory compute, analog compute
  • Extreme energy efficient inference hardware accelerators for ML loads and lower complexity algorithms and convolutional nets
  • Generic Attribution methods for deriving non NN-based solutions and NN simplification
  • Real Time 3D perception, mapping, reconstruction, and geometry interpretation
  • Eye-tracking devices and algorithms
  • Hand skeleton and multimodal human body pose estimation and tracking
  • Low power/complexity rendering systems
  • Lighting/illumination modelling
  • Multi-focal, near eye displays
  • High efficiency video coding techniques
  • Deep learning based image and video compression (intra and inter prediction, in-loop filters, transforms, entropy coding)
  • Deep learning based optimized video encoding
  • Perceptually optimized video coding
  • Image and video quality assessment
  • 6DoF video compression, Point Cloud compression
  • Novel processor architectures, microarchitectures, extensions, and accelerators
  • Multimedia and gaming architectures (not limited to GPU, GPGPU, VLIW, DSP, etc.)
  • Novel architectures for artificial intelligence, edge training and inference
  • Security features of CPUs and accelerators at the instruction set, memory system, and SOC levels
  • Isolation technologies: Virtualization, enclaves, and software sandboxing
  • Key management for IoT: Establishing trust between embedded devices
  • Machine learning model security: DRM for learning models
  • Protocol security: Analysis and verification of communication protocols
  • SoC security: Security of heterogeneous systems on chip
  • Software-based exploitation of hardware vulnerabilities: Micro-architectural attacks, side-channels, and associated countermeasures
  • User authentication: Biometric and behavioral authentication of users by mobile and embedded devices
  • Vulnerability detection: New tools and techniques for finding exploitable vulnerabilities in C software, with a focus on embedded systems
  • Defect-oriented testing and fault modeling in deep sub-micron process nodes
  • Applications of Data Analytics, Machine Learning and AI in Test
  • Test Challenges for 2.5D/3D Systems in Packages

Recent Winners

Reduction of PPA and Cost in Advanced-Node SoC Design

Faculty Advisor: Andrew Kahng
Student Fellow: Lutong Wang
Qualcomm Mentor: Tuck-Boon Chan
Awarded: Feb 2019

Privacy-preserving Federated Learning for On-Device Training of Large Machine-Learning Models

Faculty Advisor: Kamalika Chauduri
Student Fellow: Mary Anne Smart
Qualcomm Mentor: Max Welling / Matthias Reisser
Awarded: Feb 2019

Printable Zinc Batteries for Low Power IOT Wi-Fi Transceivers

Faculty Advisor: Shirley Meng & Joseph Wang
Student Fellow: Jonathan Scharf
Qualcomm Mentor: Derrick Lin
Awarded: Dec 2018

Dual Reference Edge FDC-PLL for Ultra-Low Phase Noise RF LO Synthesis

Faculty Advisor: Ian Galton
Student Fellow: Amr Eissa
Qualcomm Mentor: Yiwu Tang
Awarded: 2018/19

Dual Reference Edge FDC-PLL for Ultra-Low Phase Noise RF LO Synthesis

Faculty Advisor: Ian Galton
Student Fellow: Amr Eissa
Qualcomm Mentor: Yiwu Tangg
Awarded: 2018/19

Dual Reference Edge FDC-PLL for Ultra-Low Phase Noise RF LO Synthesis

Faculty Advisor: Duygu Kuzum
Student Fellow: Yuhan Shi
Qualcomm Mentor: Jaeyoung Kim
Awarded: 2018/19

Smart Sensors for Enhanced Radar Imaging and Localization

Faculty Advisor: Piya Pal
Student Fellow: Heng Qiao
Qualcomm Mentor: Teja Sukhavasi
Awarded: 2018/19

View All Past Winners

Jacobs School of Engineering FMA Contacts

Lisa Russon
(858) 534-4950
lrusson@ucsd.edu

Jan Dehesh
(858) 534-2329
jdehesh@ucsd.edu