Signal Processing Theory and Methods

Post-Doctoral Position in Machine Learning for Human Machine Trust in Teaming

Compensation: 85K per year + Benefits  

Contact for interview (include most recent CV + 3 references contact information) and further information

Shuchin Aeron shuchin@ece.tufts.edu, 

Matthias Scheutz Matthias.Scheutz@tufts.edu 

Description: Sponsored by AFOSR, a postdoctoral research position is sought in the area of developing machine learning algorithms and methods for estimating cognitive states from a multimodal suite of sensors measuring EEG, ECG, BP, fNIRS, eye-gaze, skin conductance, while performing a simulated or real-world task in conjunction with other workload such as conversation and active communication with a team of human or robots. The project offers a one-of-a-kind opportunity in a multi-disciplinary team setting towards building reliable prediction models for human cognitive states from physiological data, a problem that is central to many human-machine interaction settings. 

A major part of the project will center on the use and development of unsupervised and weakly supervised machine learning methods such as contrastive representation learning, and self-attention models, to leverage abundance of unlabeled data while utilizing a few strongly labeled data points to inform various cognitive states that may be present in the data. These challenges are further exacerbated by the presence of anomalies and missing data. Furthermore, to address the unique challenges that center on dealing with human data, it is anticipated that the project will require exploiting novel theory and methods in the areas of Domain Adaptation, Privacy and Fairness, Distributional Robustness, and Feature Selection. 

For a list of recent papers (2019-2020) related to the project please visit the Google Scholar profiles of 

1. PI Matthias Scheutz  https://scholar.google.com/citations?user=5yT3GScAAAAJ&hl=en and 
2. Co-PI Shuchin Aeron https://scholar.google.com/citations?user=T7TUmRMAAAAJ&hl=en 

The postdoctoral fellow will be advised jointly by PI Matthias Scheutz (CS), and Co-PIs Shuchin Aeron (ECE) and Sergio Fantini (BioEngineering) at Tufts. Additionally, the postdoctoral fellow will be exposed to new initiatives at Tufts, namely Tufts TRIPODS https://tripods.tufts.edu, Tufts Data Intensive Science Center (DISC) https://disc.tufts.eduoffering a well-rounded development for the next phase of the career.  

Eligibility requirements: PhD (CS, ECE, or BioMedical Engineering) in Information Theory or Statistical Signal Processing or Machine Learning or Applied Mathematics with applications to data science. Efficiency in coding with Python, Ability to communicate with the members of an interdisciplinary team and self-manage the project expectations + report and paper writing.

A postdoctoral scholar position with a focus on applications of machine learning in cardiac MRI. Details can be found at:

https://recruit.ap.uci.edu/JPF05862

The department of mathematics and computer science at the Weizmann Institute invites students for a PhD position in the areas of signal processing and machine learning with applications in communications, radar, medical imaging and optical imaging. The selected candidate will work with Prof. Yonina Eldar at the SAMPL lab. In the area of medical imaging, the work will be performed in close collaboration with leading hospitals in Israel and abroad. Some of the topics will be in close collaboration with PIs at MIT, Stanford and the Broad Institute.

Qualifications: Applicants should have a masters (or they are about to graduate) in electrical engineering, computer science, or applied mathematics. Excellent English writing skills are an advantage.

For more details please visit Prof. Eldar’s website: http://www.wisdom.weizmann.ac.il/~yonina/YoninaEldar/index.html

To submit your application, please send an updated CV with 2 letters of recommendation and a cover letter to yonina.eldar@weizmann.ac.il

The department of mathematics and computer science at the Weizmann Institute invites researchers for a postdoctoral position in the area of signal processing and machine learning with applications in communications, radar, medical imaging and optical imaging. The selected candidate will work with Prof. Yonina Eldar at the SAMPL lab. In the area of medical imaging, the work will be performed in close collaboration with leading hospitals in Israel and abroad. Some of the topics will be in close collaboration with PIs at MIT, Stanford and the Broad Institute.

Qualifications: Applicants should have a Ph.D. (or they are about to graduate) in electrical engineering, computer science, or applied mathematics. A strong background in signal processing, algorithms or mathematics is required. Excellent English writing skills are an advantage.

Compensation: This is a full-time, one-year, non-tenure-track appointment with possibility of extension subject to satisfactory performance. Funds for conference travel and research expenses will also be provided. Starting date is flexible.

For more details please visit Prof. Eldar’s website: http://www.wisdom.weizmann.ac.il/~yonina/YoninaEldar/index.html

To submit your application, please send an updated CV with a list of publications, 3 letters of recommendation and a cover letter to yonina.eldar@weizmann.ac.il

DSP/FPGA Engineer (Mid-to-Senior Level Position)

Are you seeking a truly creative, challenging technical opportunity? Does product development excite you more than filling out a timecard? Does working in a tightly integrated hardware and software team where thinking outside the box is rewarded sound exciting? Do you want to be a part of a high visibility effort to create a next-generation Counter UAS (C-UAS) system and get paid to work with drones?

CPC, specializes in designing and building hardware and software to meet our customers’ next requirement. We focus on offering devices that can be fielded immediately to meet the mission needs that lie directly ahead. Size, weight, power, and packaging are of paramount importance in everything we design and build. Our expertise allows us to rapidly provide these solutions in specialized electronic/mechanical design, embedded & application-level software, reverse engineering & protocol analysis, and RF signals and communications systems. U.S. Citizenship required. Successful candidates must be willing and able to obtain a security clearance and pass a pre-employment drug screen.

We are seeking a Principal DSP/FPGA Engineer to work on the development of our C-UAS product line. This role requires understanding of both theoretical and practical aspects of designing DSP/FPGA solutions for RF products. An ideal candidate will have demonstrable experience designing such solutions in a commercial and/or government environment with aggressive time-scales.

This role requires the ability to effectively collaborate with a cross functional design team. Candidates must possess solid interpersonal as well as excellent oral and written communication skills.

Ideal Candidates will be proficient with:

• Communications systems, associated algorithms and their design. This includes an understanding of RF filters, signal modulation/demodulation, channel equalization/channel estimation, channel coding (FEC), RF signal acquisition, RF frequency control (AFC), RF gain control (AGC)
• DSP baseband signal processing and task scheduling
• DSP Peripheral programming and control
• Waveform generation and transmission using software defined radios (SDR)
• Programming DSP and FPGA devices using C/C++, Python, MATLAB/Simulink and VHDL
• Interpreting and implementing wireless standards such as 3GPP (LTE, WCDMA, GSM), IEEE 802.11 (WIFI), IEEE 802.16 (WiMAX), and Frequency-hopping spread spectrum (FHSS)
• Mixed signal requirements including quantization bits, dynamic range (SFDR), power consumption and the design of baseband equivalent models. Experience designing solutions to overcome imperfections in the RF signal path​

Preferred skills will include:

• Fixed-point DSP design
• Hardware design for DSP/FGPA based systems and their peripherals
• Experience with signal/bit analysis and visualization tools
• Programming DSP algorithms using X-MIDAS, and M2K (Midas 2000)
• Embedded software development
• System architecture design and the partitioning of functionality between GPP, DSP and FPGA
• Version control systems (Git, Subversion or similar)

Minimum Qualifications:

• BS Electrical Engineering (or similar field) with 7+ years of experience designing DSP/FPGA solutions, or MS with 5+ years of experience

We seek individuals who:

• are enthusiastic about embedded development
• are excited about working closely on small development teams,
• enjoy taking ownership of their work,
• want to help us brainstorm and design new solutions,
• want to introduce new technology ideas, and
• want to be technically challenged, and to learn.

 We work in a unique environment that’s employee and family-friendly. Our employees enjoy:

• Generous salary, holidays, vacation and sick leave;
• Highly subsidized health, dental plans and life insurance;
• 401k plan w/company match that vests immediately;
• Private or semi-private window offices;
• Tuition/educational assistance program;
• Casual dress and flexible schedule;

We are proud to be an Equal Opportunity Employer and do not discriminate on the basis of race, religion, gender, gender identity, national origin, color, age, military service eligibility or veteran status, disability, sexual orientation, gender identity, marital status or any other protected class. We encourage and support workplace diversity.

U.S. citizenship is required for this position.   An Equal Opportunity Employer M/F/D/V.

No third parties, no agencies, no subcontractors need apply.

About the position:

We have a vacancy for a PhD Research Fellow position at the Department of Electronic Systems (IES). The PhD position is for up to 4 years with 25% work assignments for NTNU IES.

Job description:

In the emerging paradigms of CPS and IoT, large quantities of data are constantly collected by multiple sensors to enable accurate inference and smart decision making. The collected data may reveal more information than required because the sensors observe multiple correlated processes. In addition, stringent limitations on IoT sensors often preclude cryptography-based data security, which makes the system vulnerable various types of physical-layer attacks, e.g., data falsification and replay attacks. Examples include unauthorized drone tracking and steering, and data falsification in autonomous transport systems. In summary, to realize the full potential promised by IoT, developed solutions need to incorporate more realistic environments and time-critical constraints to ensure privacy-preserving and attack-resilient network operation.

The aim of this project is to design and analyze advanced distributed signal processing and optimization approaches to overcome security and privacy challenges faced by future CPS/IoT, where traditional methods fail to prevent attacks and loss of privacy. The PhD candidate will be affiliated with NTNU IoT lab, and have the opportunity to visit and collaborate with research scientists from SINTEF, University of Notre Dame, USA and Syracuse University, USA.

Qualification requirements:

We seek a highly-motivated individual who has

• strong background in mathematics, communications, and statistical signal processing
• research-oriented master thesis in a related field, e.g., statistical signal processing, information theory, statistical machine learning, multi-agent networked systems, applied mathematics, or optimization
• experience with programming
• good written and oral English language skills

Salary and conditions:

PhD candidates are remunerated in code 1017, and are normally remunerated at gross from NOK 479 600 per annum before tax. From the salary, 2% is deducted as a contribution to the Norwegian Public Service Pension Fund.

For more information and application submission, please follow the link:
https://www.jobbnorge.no/en/available-jobs/job/174696/phd-position-in-distributed-signal-processing-for-resilient-iot-cps

Immediate opening for a postdoctoral researcher in radionavigation and wireless communication systems at the Autonomous Systems Perception, Intelligence, & Navigation Laboratory (https://aspin.ucr.edu); University of California, Irvine (https://uci.edu).

Desired qualifications: (1) Ph.D. in Electrical and Computer Engineering, Aerospace Engineering, or a closely related field; (2) knowledge of wireless communications and/or GNSS; (3) demonstrated hands-on expertise in software-defined radio (SDR) design and implementation; (4) strong programming skills (MATLAB, LabVIEW, and/or C++); (5) publications in relevant top-tier journals and conference proceedings; (6) strong communication, presentation, and documentation skills; and (7) ambition to become a leader in the field (e.g., Professor, Senior R&D Engineer, Managing Technical Director, Program Manager, etc.). To apply, please email me: (1) a complete CV, (2) up-to-date university transcripts, (3) a short statement describing research interests and goals, (4) names and contacts of 3 references, and (5) a link to your website (if available). Position duration: minimum of 1 year, extendable up to 3 years.

Algorithms for Event-Driven Camera Analysis

School of Computing, Engineering and Mathematics

Scholarship code: 2019-089

https://www.westernsydney.edu.au/graduate_research_school/graduate_research_school/scholarships/current_scholarships/current_scholarships/scem_algorithms_for_event-driven_camera_analysis

About the project

Event-driven cameras are exciting technology that do not acquire full images like traditional cameras, but record only intensity changes when they occur. The International Centre for Neuromorphic Systems at Western Sydney University has been adapting them to perform Neuromorphic space imaging.

This PhD scholarship builds on this work to help develop the correct abstraction and a theory so as to improve knowledge extraction algorithms. It goes from modelling to algorithm testing using real data, working together with a world-class team.

What does the scholarship provide?

• Domestic candidates will receive a tax-free stipend of $30,000(AUD) per annum for up to 3 years to support living costs, supported by the Research Training Program (RTP) Fee Offset.
• International candidates will receive a tax-free stipend of $30,000(AUD) per annum for up to 3 years to support living costs. Those with a strong track record will be eligible for a tuition fee waiver.
• Support for conference attendance, fieldwork and additional costs as approved by the School.

International candidates are required to hold an Overseas Student Health Care (OSHC)(opens in new window)insurance policy for the duration their study in Australia. This cost is not covered by the scholarship.

Eligibility criteria

The successful applicant should:

• hold qualifications and experience equal to a Masters in the fields of Data Science, Computer Science, Applied Mathematics, Electrical Engineering or similar fields.
• have programming experience (ideally Python)
• be knowledgeable in topics such as signal processing, algorithms, probability and statistics.
• have the ability to think abstractly and deeply.
• have the ability to work in a team of highly motivated engineers, computer and data scientists, and applied mathematicians.
• be highly motivated to tackle challenging problems, with a desire to learn and grow.

Educational Testing Service (ETS), with headquarters in Princeton, NJ, is the world’s premier educational measurement institution and a leader in educational research. With more than 3,400 global employees, we develop, administer and score more than 50 million tests annually in more than 180 countries at more than 9,000 locations worldwide. We design our assessments with industry-leading insight, rigorous research and an uncompromising commitment to quality so that we can help education and workplace communities make informed decisions.

ETS's Research & Development division has an opening for an associate research scientist in the Speech area of the NLP & Speech research center. The projects in this research group focus on the application of NLP, speech, dialogue, and multimodal processing algorithms in automated scoring capabilities for assessment and learning tasks involving constructed responses (such as essays and spoken responses). In addition to its signature automated scoring systems for text and speech assessments, the group is actively pursuing technology innovation in education and learning spaces by performing foundational research, prototyping next-generation capabilities, and collaborating with academic and industry partners. The NLP & Speech research group currently comprises around 20 PhD-level research scientists, 10 research engineers, and 10 research assistants and administrative staff.

This is an excellent opportunity to be part of a world-renowned research and development team and have a significant impact on existing and next-generation NLP, speech, dialogue, and multimodal systems and their use in educational applications.

Responsibilities:

• Conceptualizing, proposing, obtaining funding for, and directing small projects in the area speech processing for educational applications and assisting in moderate-to-major research projects. 
• Assist in generating or contributing to new knowledge or capabilities in the field of speech processing and in applying that new knowledge and capabilities to existing and/or new ETS products and services.  Some speech processing research areas that are relevant to ETS R&D include (but are not limited to) automatic speech recognition for non-native speakers of English, voice biometrics, computer-assisted pronunciation training, and automated speech scoring.
• Participate in setting substantive research and development goals and priorities for a group or initiative within a vice presidential area.
• Develop proposals and budgets for small projects and/or assist in development for moderate-to-major ones.

Requires:

• Ph.D. in Computer Science, Computational Linguistics, Electrical Engineering, Natural Language Processing, Linguistics, or a similar area with major education in speech processing.
• At least one year of independent substantive research experience in speech processing is required. Experience can be gained through doctoral studies.
• Practical expertise with speech processing tools (e.g., kaldi), experience with machine learning toolkits (e.g., Weka, scikit-learn), and fluency in at least one major programming language (e.g. Java, Python).
• Practical experience with deep learning paradigms and toolkits (e.g., TensorFlow, pytorch, Keras) is highly desirable.
• Documented contributions to research communities and a strong publication record and experience I system building and prototyping are preferred.

 We offer a competitive salary, comprehensive benefits, possible relocation assistance and excellent opportunities for professional and personal growth. For a full list of position responsibilities and to apply please visit the following link: Associate Research Scientist, Speech

EDUCATIONAL TESTING SERVICE is an Equal Opportunity and Affirmative Action Employer of Women and Minorities.

EDUCATIONAL TESTING SERVICE is an Equal Opportunity and Affirmative Action Employer of protected Veterans and Individuals with Disabilities.

EDUCATIONAL TESTING SERVICE is a Drug-free workplace.

Applications are invited for a postdoctoral position in the area of machine learning and data analytics for human performance understanding and prediction, a collaborative effort at Tufts University among the Department of Electrical and Computer Engineering, Department of Computer Science, the Center for Applied Brain and Cognitive Sciences (CABCS) at Tufts University and the U.S. Army Combat Capabilities Development Command Soldier Center at Natick., MA This appointment would be for 12-18 months with an estimated start date of October 2019.

The primary project is entitled “Real time prediction of individual and team performance metric from neurophysiological measurements and team interaction data”. Under this project, the fellow will work with Tufts faculty, Drs. Shuchin Aeron, Michael Hughes, and Eric Miller, as well as CABCS scientists to develop supervised and semi-supervised machine learning algorithms that are capable of predicting cognitive state (e.g. stress) and task performance metrics (e.g. speed or marksmanship) from labeled and unlabeled multimodal physiological sensor data including information collected continuously as a function of time (e.g. accelerometer recordings or GPS trajectories) as well as data at a relatively few points in time before, during, and after a specific task (e.g. surveys and performance evaluations). 

In addition to assessing individuals, data will be collected to support the characterization of team and intergroup dynamics. We anticipate the effort will require the use of classical as well as recent developments in machine learning and in particular recurrent neural networks, deep generative models, manifold learning, and social network analysis. 

While previous experience in theoretical and applied machine learning would be ideal, we welcome applicants with significant experience in related fields including information theory, statistical signal processing, sparse signal or image processing, compressive sensing, and distributed convex optimization.