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Research Scientist for Research on Wireless Power Transfer

The hired Research Scientist will work on the research of wireless power transfer (WPT) in the Biomedical EM Lab at Singapore University of Technology and Design (http://people.sutd.edu.sg/~huangshaoying/). The contract is 1 year (renewable) for research assistants.

Project Description

The project is to investigate the electromagnetic mechanisms for a compact, efficient, and wideband antenna/radiator suitable for wireless energy harvesting (WEH)/WPT. This exploration will span both conductive and magnetoelectric (ME) antennas. Our study will consider WIFI, power from base stations, and WPT as potential power sources.

Job Scopes:

• Literature review of the research domain
• Investigations on the electromagnetic mechanisms for a compact, efficient, and wideband antenna/radiator suitable for WEH/WPT
• Designs of small antennas/radiators for WEH/WPT
• Antenna/radiator fabrications and testing
• Paper writing and documentations

Your profile

We are looking for a researcher with the following expertise and skills,

• A PhD in electrical engineering, physics, or related field
• Knowledge on EM theories microwave engineering, especially antenna theories and transmission line theories
• A background on EM modeling and simulation software (e.g., CST)
• Research or design experience on WEH/WPT and/or antenna designs
• Testing and characterization of RF/microwave devices or components
• Python/Matlab coding
• Good track record of the capacity to drive first author publications
• Good communication skills and interpersonal skills
• A good team player

For interested candidates

Interested candidate please send your CV to Huang Shaoying at huangshaoying@sutd.edu.sg

Job Description:

• Applied Data Science on Test Yield Analysis & Improvement OpportunityProduct Test Yield and Key Test Parameters Analysis in relationship & correlation to Fab WET data or Wafer Probe Die Sort Data
  Develop a predictive model of product test yield based on available key Fab, Sort monitoring data
  Leveraging on advance data analytics and Artificial Intelligence technique to enable speed of detection, prevention and prediction
  Collaborate with respective product owner PE to drive yield improvement opportunity based on big data analysis or specific excursion with timely detection of abnormality
  
• Quality Monitoring Data & Customer Complaint Analysis in relations to Test Information & Manufacturing Process DataAnalyze real-time monitoring data abnormality (Maverick) detection & prevention measures effectiveness for applicable semiconductor manufacturing processes.
  Leverage predictive analytics to trigger interventions that reduce the risk of yield loss or quality issues.
  Collaborate with respective product owner PE to drive out-quality improvement opportunity based on big data analysis & predictive quality risk
  
• Collaborate with Manufacturing Site to Drive Data-Driven Process Optimization for Yield and Quality ImprovementWork closely with on-site Product, Test or Process engineers to analyze production data, identify trends, and optimize manufacturing processes.
  Use data science techniques to reduce variability and improve overall product quality.
  Leverage on machine learning models application opportunity in Fab, Die probe, Die-to-Reel, Assembly, Test, Tape & Reel process for total continuous improvement & innovation.
  
• Failure Mode Analysis & Customer Risk/RMA MitigationEnhance product quality & reliability by developing predictive models that assess the potential quality risk to customers. Use insights from these models for disposition decision making to ensure compliance with quality standards.
  Collaborate with quality, process, product engineer on in-depth failure mode and effects analysis (FMEA) using historical and real-time data to predict and mitigate potential defects.
  Collaborate with manufacturing teams to implement corrective actions based on predictive insights.
  
• Additional Responsibilities:Track wafer and package yield trends on a daily basis
  Leverage 2DID and Die Level Traceability to perform novel analyses and move yield/quality issues upstream to wafer level whenever possible
  Identify opportunities for cost reduction
  Generate analyses aimed at highlighting device sensitivities and interactions between design/fab/assy/test
  Leverage all available test points to detect anomalies, shifts, drifts, outliers, maverick events as close to the Point of Occurrence as possible
  Identify opportunities for cost reduction
  Monitor/Report on KPI's and facilitate data-driven decision making
  

Requirements:

• Domain Expertise:Bachelor’s or Master degree in Data Science, Computer Science, Electrical Engineering, or a related field, with specialized knowledge in semiconductor manufacturing processes. Experience in predictive analytics for yield and quality in semiconductor fabs is highly desirable
  
• Technical Skills:Proficiency in programming languages such as Python or R, with preferred experience in machine learning libraries and statistical modeling. Strong background in data analysis, including the use of big data technologies and cloud platforms
  
• Experience with Semiconductor Manufacturing:At least 3 years of experience working in semiconductor manufacturing, particularly in roles focused on process monitoring, yield improvement, and quality control. Familiarity with Fab, Die probe, and Assembly processes is essential
  
• Analytical and Predictive Modeling Expertise:Proven track record in developing and deploying predictive models for yield analysis and quality assurance. Experience with tools like JMP, Minitab, or similar for statistical process control (SPC) and predictive maintenance is a plus.
  
• Cross-Functional Collaboration and Communication:Strong communication skills to effectively collaborate with process engineers, quality assurance teams, and other stakeholders. Ability to translate complex data insights into actionable strategies that improve product yield and quality.
  

Interested applicants, please apply via our corporate website under the requisition number 8439 at http://www.qorvo.com/careers/ with a copy of your detailed resume. ​

We have several available postdoctoral and research assistant positions at the Singapore University of Technology and Design (SUTD) to work in the area of Robotics. This position focus on supporting the design of algorithm for various robotics applications.

Qualification:
- Relevant Degree in Electrical Engineering, , Mechanical Engineering, Robotics, or related areas
- Strong hands on experience Preference will be given to those with prior robotics project experience such as:
1) SLAM;
2) ROS;
3) Sensor fusion;
4) Machine Learning;
5) Mobile platforms (eg: wheeled robots, legged robots); and
6) Manipulators

Applications, assembled as a single PDF file, should contain a complete resume, full list of publications, as well as the names and contact information of up to three references.

Interested applicants may send his/her CV to Assoc/Prof U-Xuan Tan via email ( uxuan_tan@sutd.edu.sg ). Review of applications will continue until the position is filled. Only shortlisted candidates will be notified.

Research Assistant/Associate positions are available in the Information Systems Technology and Design (ISTD) Pillar, Singapore University of Technology and Design (SUTD). The candidates will conduct research in acoustic sensing and simultaneous localization and mapping (SLAM).

Interested applicants should send their CVs, transcripts, and representative publications to qun_song@sutd.edu.sg. Successful candidates will directly work with Dr. Qun Song.

Qualification & Experience:

• Bachelor's (Research Assistant) / Master's (Research Associate) degree in Computer Science / Electrical Engineering / Mathathematics or related fields with good grades.
• Solid programming skills and prototyping capabilities.
• Research experience in sensing, signal processing, deep learning, robotics, SLAM, and related domains is advantageous.

We are seeking machine learning and image recognition specialist to join our project team. The ideal candidate preferably has the background in machine learning techniques and image recognition algorithms to solve complex problems and improve project outcomes. You will work closely with a multidisciplinary team to develop, implement, and optimize ML models that are critical to the success of our projects.

• Design, develop, and deploy machine learning models and image recognition algorithms tailored to project-specific requirements.
• Collaborate with cross-functional teams to understand project needs, gather, and analyse requirements, and deliver ML-driven solutions.
• Conduct data collection, preprocessing, and augmentation to create training datasets.
• Implement and optimize image recognition models for accuracy, efficiency, and scalability.
• Evaluate model performance using appropriate metrics and make necessary adjustments to improve outcomes.
• Stay abreast of developments in machine learning and image recognition fields to incorporate cutting-edge technologies and methodologies into project solutions.
• Document model development processes, algorithms, and performance metrics.
• Provide technical guidance and mentorship to team members on machine learning and image recognition best practices.

Required/Desired Skills

• Experience in machine learning and image recognition, with a strong portfolio of project work that demonstrates expertise in these areas.
• Proficiency in programming languages such as Python, and familiarity with ML libraries (e.g., TensorFlow, PyTorch) and computer vision libraries (e.g., OpenCV).
• Experience with deep learning architectures (CNNs, RNNs, GANs) specifically designed for image processing tasks.
• Analytical skills with the ability to solve complex problems independently and as part of a team.

Required/Desired Qualifications

• Bachelor’s or master’s degree in Electronics & computer science, Artificial Intelligence, or a related field.

Experiences

Minimum 3 years’ experience in machine learning and image recognition within a technology/research organisation. Fresh graduate with relevant knowledge and skills will be considered.