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Schlumberger Foundation Faculty For the Future Fellowship

About the Schlumberger Foundation

The Schlumberger Foundation is a nonprofit organization that supports science and technology education. Recognizing the link between science, technology, and socio-economic development, as well as the key role of education in realizing individual potential, the Schlumberger Foundation flagship program is Faculty for the Future.

About Faculty for the Future

The program’s long-term goal is to accelerate gender equality in STEM by generating conditions that result in more women pursuing scientific careers through alleviating some of the barriers they encounter when enrolling in STEM disciplines. The program is committed to gender parity in science in the interests of sustainable development and recognizes that full access to and participation in a STEM curriculum is essential for the empowerment of women and girls. By accelerating gender equality in STEM, the talent and capacities of these women can be developed for the benefit of their local communities, regions and nations.

The program awards fellowships for advanced research in STEM at leading research institutes abroad. Faculty for the Future Fellows are expected to return to their home countries upon completion of their studies to contribute to the economic, social and technological advancement of their home regions by strengthening the STEM teaching and research faculties of their home institutions as well as through their leadership in science-based entrepreneurship. They are also expected to contribute to the public sector where their newly acquired technical and scientific skills can help provide evidence-based support for STEM policy making, including topics of gender representation.

This program acts as a catalyst for these women to further tap into their potential. Through heightened motivation, sharpened self-awareness and a lasting passion for science they in turn capture the imagination of other women and girls around them to regard scientific pursuits as a necessary means towards advancement and growth.

Since its launch in 2004, 739 women from 82 countries have received Faculty for the Future fellowships for PhD and Post-Doctorate STEM research programs. Through interactive online tools and in-person meetings, the program provides a platform for these women to take joint action in identifying and unravelling the impediments that are holding back equal opportunities in STEM education and careers in their local communities and home countries.

Apply

​Applications will be received online at www.fftf.slb.com until November 9th, 2020​. Any questions should be directed to Eve Millon (emillon@slb.com​)​​​. ​

Categories
Announcements Jobs/Internships

Future Faculty Program – 2021 Cohort Application Open

The Future Faculty Program (FFP) was introduced to increase the number of Ph.D. graduates who obtain academic positions, in particular faculty positions in prestigious engineering and computer science schools. In addition, the FFP is intended to improve the preparation of students for these academic careers, so that students can better succeed once they obtain such a position. Students admitted to the program are designated Future Faculty Fellows.

Eligible applicants are doctoral students enrolled in the Clark School or the Department of Computer Science who have advanced to candidacy OR completed coursework and qualifying exams. When students apply, the application system will request letters of recommendation from their research advisors and one additional faculty member. A doctoral student’s research advisor will be required to engage in mentoring the student as a Fellow, so the commitment to actively monitor his or her progress towards preparing to apply for a faculty position should be explicitly stated in the recommendation letter. Fellows are chosen based on their motivation for becoming a faculty member and their potential for securing a tenure-track faculty position at a top-50 academic institution. We expect to choose up to 24 new fellows for Spring 2021.

For more information about the FFP and application instructions, please visit: https://eng.umd.edu/future-faculty-program.

The deadline for receipt of completed applications is November 12, 2020. Letters of recommendation are due November 20, 2020. Accepted students will be notified by the end of the Fall semester and must enroll in the one-credit course ENES601 in Spring 2020, which meets Thursdays, 3:30 – 4:45pm.

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Defenses

Dissertation Defense: Donald “Bucket” Costello

Title: CERTIFYING AN AUTONOMOUS SYSTEM TO COMPLETE TASKS CURRENTLY RESERVED FOR QUALIFIED PILOTS

Author: Donald “Bucket” Costello

Date/Time: Oct 29, 2020 07:00 PM Eastern Time (US and Canada)

Examining Committee:

  • Asst. Prof. Huan Xu, Chair/Advisor
  • Prof. Adam Porter, Dean’s Representative
  • Prof. Jeffrey Herrmann
  • Prof. Miao Yu
  • Asst. Prof. Sarah Bergbreiter

Abstract: When naval certification officials issue a safety of flight clearance, they are certifying that when the vehicle is used by a qualified pilot they can safety accomplish their mission. The pilot is ultimately responsible for the vehicle. While the naval safety of flight clearance process is an engineering based risk mitigation process, the qualification process for military pilots is largely a trust process. When a commanding officer designates a pilot as being fully qualified, they are placing their trust in the pilot’s decision making abilities during off nominal conditions. The advent of autonomous systems will shift this established paradigm as there will no longer be a human in the loop who is responsible for the vehicle. Yet, a method for certifying an autonomous vehicle to make decisions currently reserved for qualified pilots does not exist. We propose and exercise a methodology for certifying an autonomous system to complete tasks currently reserved for qualified pilots. First, we decompose the steps currently taken by qualified pilots to the basic requirements. We then develop a specification which defines the envelope where a system can exhibit autonomous behavior. Following a formal methods approach to analyzing the specification, we developed a protocol that software developers can use to ensure the vehicle will remain within the clearance envelope when operating autonomously. Second, we analyze flight test data of an autonomous system completing a task currently re-served for qualified pilots while focusing on legacy test and evaluation methods to determine suitability for obtaining a certification. We found that the system could complete the task under controlled conditions. However, when faced with conditions that were not anticipated (situations where a pilot uses their judgment) the vehicle was unable to complete the task. Third, we highlight an issue with the use of onboard sensors to build the situational awareness of an autonomous system. As those sensors degrade, a point exists where the situational awareness provided is insufficient for sound aeronautical decisions. We demonstrate (through modeling and simulation) an objective measure for adequate situational awareness (subjective end) to complete a task currently reserved for qualified pilots.


Categories
Workshops, Seminars, & Events

Imminent Events/Seminars – The Institute for Systems Research

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PLEASE NOTE THAT THE FOLLOWING EVENTS WILL BE HOSTED VIRTUALLY, AND THAT MANY OF THEM REQUIRE ADVANCE REGISTRATION.

WEDNESDAY, OCTOBER 14, 2020

12:00 P.M.-1:00 P.M.  Policies and Strategies to Improve Energy Affordability

THURSDAY, OCTOBER 15, 2020

9:30 A.M.-11:00 A.M.  CCSP Seminar: Amin Aminzadeh Gohari, The Auxiliary Receiver Approach in Network Information Theory

11:00 A.M.-12:30 P.M.  NEXTOR III The Pandemic and Aviation Webinar #3: Air Navigation Service Providers/Flight Operations

12:00 P.M.-1:00 P.M.  IEEE Leadership Seminar: “Pathways from an Engineering Degree”

FRIDAY, OCTOBER 16, 2020

1:00 P.M.  MSE Seminar: Returning the favor: ALD for catalysts, or catalytic reactions for ALD?

2:00 P.M.  Lockheed Martin Robotics Seminar: Enhancing Human Capability with Intelligent Machine Teammates

MONDAY, OCTOBER 19, 2020

12:00 P.M.-2:00 P.M.  School of Public Policy Election Series: Empowering Voters–An Accessible and Full Ballot Box

3:00 P.M.  UMD Faculty and Staff Convocation

TUESDAY, OCTOBER 20, 2020

11:00 A.M.  ChBE Seminar: Atomic and molecular layer deposition of redox-active thin films

4:00 P.M.-4:30 P.M.  MAMNA Virtual Seminar: Daniel Cohen, Princeton Univ, “Study & Control of Collective Cell Behavior”

WEDNESDAY, OCTOBER 21, 2020

1:30 P.M.  CS Seminar: Jayesh Gupta, “Deep Implicit Coordination Graphs for Multi-agent Reinforcement Learning”

4:30 P.M.-5:30 P.M.  Lockheed Martin Sikorsky Fall 2020 Colloquium

THURSDAY, OCTOBER 22, 2020

10:30 A.M.-12:00 A.M.  Adding Resilience to the Energy Equation

4:00 P.M.-5:00 P.M.  UMD Distinguished Scholar-Teacher Lecture by Professor Derek A. Paley

5:00 P.M.-6:00 P.M.  The Student Advantage: Why You Should Start Your Company Today

FRIDAY, OCTOBER 23, 2020

1:00 P.M.  MSE Seminar: How to Destroy a Satellite in Three Easy Steps, and How Material Scientists Can Help!

2:00 P.M.  Maryland Robotics Student Seminar: Instant Segmentation of Oysters in the Chesapeake Bay

3:30 P.M.-4:30 P.M.  Booz Allen Hamilton Colloquium: Rachael Myers-Ward, U.S. Naval Research Lab

For a full listing of ISR events, visit the ISR website at:   https://isr.umd.edu/events/index.php

Please submit upcoming events by visiting the ISR website events page or by clicking the following link:  Submit an event to the ISR calendar.   

Categories
Jobs/Internships

Tenure-Track Faculty Position in Mechanical Engineering – Wayne State University

The Mechanical Engineering Department at Wayne State University invites candidates to apply  for a tenure-track faculty position. We expect to make the appointment at the Assistant Professor  level, but will consider exceptional senior-level candidates with a strong track record of major  external funding and peer-reviewed publications. 

Applicants must have an earned doctorate in mechanical engineering or related fields with a  concentration in one of the following two areas: 

  1. design and advanced manufacturing systems, hybrid manufacturing processes,  implementation of artificial intelligence and machine learning in design and manufacturing,  materials informatics and big data design and fabrication of metamaterials, characterization,  processing and synthesis of materials, metal Additive Manufacturing processes, scalable and  adaptable manufacturing processes. 
  2. robotics, autonomous systems, multi-agent dynamic systems, advanced mechatronic systems  and electric vehicles. 

Duties involve teaching at both undergraduate and graduate levels, introducing new courses in the applicant’s respective field of expertise, developing an externally-funded research program,  supervising and mentoring both graduate and undergraduate students, and engaging in professional  societies and university services.  

The anticipated starting date of employment is August 18, 2021. This position is a 9 month  appointment. All applicants must submit their applications through the WSU Online Hiring  System at https://jobs.wayne.edu posting No. 044671.

Electronic applications must include

  • A letter  of application
  • Curriculum vita
  • Names, Addresses, and Contact information for at least three references,
  • A personal statement regarding teaching and research activities (limited to 3 pages), 
  • selected refereed journal publications (limited to 3 papers). Faxed or emailed applications  will not be accepted.

The review of applications will begin on November 1, 2020 and the search  will remain open until the position is filled. Salary and rank of position will be commensurate with  qualifications and experience. 

Wayne State University is a premier, public, urban research university located in the heart of  Detroit where students from all backgrounds are offered a rich, high quality education. Our deep  rooted commitment to excellence, collaboration, integrity, diversity and inclusion creates  exceptional educational opportunities preparing students for success in a diverse, global society.  WSU encourages applications from women, people of color, and other underrepresented people.  Wayne State is an affirmative action/equal opportunity employer.

Categories
Workshops, Seminars, & Events

AI Tracks at Sea Challenge – A student Challenge Grant Opportunity

Announcing the  “AI Tracks at SEA Challenge” webinar (see attached) to be held on Monday Oct 19th at 330pm EST.  This challenge solicits software solutions to automatically generate georeferenced tracks of maritime vessel traffic based on data recorded from a single electro-optical camera imaging the traffic from a moving platform.  Note that this challenge is being run by the Naval Information Warfare Center, a sister lab to Carderock. 

Note that this challenge is open only to students at accredited higher education institutions. 

Details for the Zoom webinar: 

When: Oct 19, 2020 03:30 PM Eastern Time (US and Canada) 

Topic: AI TRACKS AT SEA CHALLENGE

Eligibility: Open to only students at accredited higher education institutions

Prizes will be awarded for the 1st, 2nd, 3rd, 4th, 5th, 6th, and 7th places in the amounts of:

1. $55,000
2. $45,000
3. $35,000
4. $30,000
5. $20,000
6. $10,000
7. $5,000

Prizes will be disbursed to the respective teams’ department, college, laboratory, or other eligible university component as specified by the consensus of the tea

Register in advance for this webinar: https://www.zoomgov.com/webinar/register/WN_c-y8O94VRfWEA2-UlHPeXQ

Submission Dates: 10/01/2020 – 12/02/2020

For more info, visit www.challenge.gov/challenge/AI-tracks-at-sea/

Categories
Workshops, Seminars, & Events

Lockheed Martin Sikorsky Fall Colloquium

Event:  Lockheed Martin Sikorsky Fall 2020 Colloquium
Location:  Virtual (register to receive link)
Date:  Wednesday, October 21, 2020
Time:  4:30-5:30 pm

Registration required. Register at 
https://go.umd.edu/Sikorsky2020

Colloquium Keynote Speaker: Christiaan Corry

Christiaan Corry is an Experimental Test Pilot at Sikorsky, a Lockheed Martin Company. He joined Sikorsky in 2008 after having spent 11 years as an officer in the United States Marine Corps where he flew the CH-53E. Christiaan started his career at Sikorsky as a Production Test Pilot providing both production and training support in all of Sikorsky’s military and commercial helicopters while also working as a project pilot on the CH-5K program. In 2016 he attended the National Test Pilot School in Mojave, CA and began working as an Experimental Test Pilot. In 2018 he joined the Future Vertical Lift program where he has supported the S-97 Raider and S-100 Defiant as a project pilot. He is the lead project pilot on Sikorsky’s Future Attack and Reconnaissance Aircraft program.
Christiaan has flown over 4,000 hours in over 25 types of aircraft. He is a graduate of the University of North Carolina, Chapel Hill with a BA in Political Science. He lives in Jupiter, FL.
For more information, contact Anna Lee at annalee@umd.edu.

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Categories
Jobs/Internships

Academic Job Vacancies – Engineeroxy

Engineeroxy is pleased to present you the next edition of our specialized newsletter including your academic and research job vacancies in schools of engineering and technology recently posted at Engineeroxy.com worldwide.

United States

Australia

Belgium

Canada

Chile

China

Germany

United Kingdom

See All Postings Here!

Categories
Jobs/Internships

Postdoctoral Research Associate – Machine Learning for Complex System Prognostics

Apply now 

Date: Oct 8, 2020

Location: Oak Ridge, TN, US, 37830

Company: Oak Ridge National Laboratory

Requisition Id 4156 

Overview: 

The Neutron Sciences Directorate (NScD) at Oak Ridge National Laboratory (ORNL) operates the High Flux Isotope Reactor (HFIR), the United States’ highest flux reactor based neutron source, and the Spallation Neutron Source (SNS), the world’s most intense pulsed accelerator based neutron source. Together these facilities operate 30 instruments for neutron scattering research, each year carrying out in excess of 1,000 experiments in the physical, chemical, materials, biological and medical sciences. HFIR also provides unique facilities for isotope production and neutron irradiation. To learn more about Neutron Sciences at ORNL go to: http://neutrons.ornl.gov. Oak Ridge National Laboratory is also a leader in computational and computer science, with unique strengths in high-performance computing and data analytics with applications to the physical and biological sciences. 

We are seeking a postdoctoral research associate who will focus on signal processing, statistical analysis, probabilistic theory and machine learning with emphasis on diagnostics and prognostics applications. This position resides in the Accelerator Science and Technology Section in the Research Accelerator Division, Neutron Sciences Diretorate at Oak Ridge National Laboratory (ORNL).  

As part of our research team, you will work with accelerator and target systems specialists and machine learning experts to develop, integrate and apply machine learning methods to improve performance of the SNS 1.4 MW accelerator and target systems.

Major Duties/Responsibilities: 

  • Develop, implement and apply new machine-learning (ML) and statistical methods techniques to sensor and component health monitoring, anomaly detection and fault isolation
  • Develop and apply both first-principles-based and data-driven techniques to solving complex engineering problems
  • Perform uncertainty quantification and uncertainty propagation analyses

Basic Qualifications:

  • PhD in nuclear, electrical engineering, mechanical, computer engineering, or engineering physics, computational science or a related field within the past 5 years.
  • Experience with open-source machine-learning tools, such as TensorFlow, pyTorch or Keras

Preferred Qualifications:

  • Experience with applying and deploying recent machine-learning methods for solving complex engineering problems, including diagnostics and prognostics of complex engineered systems
  • Experience in physics-informed machine learning for analysis of physical systems
  • Experience with uncertainty quantification methods and application of those methods in complex systems
  • Experience working in Linux environments on large high-performance cluster computing architectures
  • Demonstrated experience in statistical methods and machine-learning methods, with a specific application to time-series datasets from multiple sensors
  • Strong understanding of underlying mathematics of signal processing, filtering and machine learning to unfold unique signatures in typical noisy time-series data
  • Demonstrated results-oriented problem-solving skills, and a willingness to apply those skills to a variety of engineering problems
  • Excellent communication skills (verbal, presentation and scientific writing) that enable effective interaction with technical peers, program managers, and sponsors
  • Strong scholarly and publication record that demonstrates independence and initiative taking
  • Ability to work independently and in a team environment, thoroughly document work performed

Additional Information:

Applicants cannot have received their Ph.D. more than five years prior to the date of application and must complete all degree requirements before starting their appointment. The appointment length will be up to 24 months with the potential for extension. Initial appointments and extensions are subject to performance and availability of funding.

This position will remain open for a minimum of 5 days after which it will close when a qualified candidate is identified and/or hired.

We accept Word (.doc, .docx), Adobe (unsecured .pdf), Rich Text Format (.rtf), and HTML (.htm, .html) up to 5MB in size. Resumes from third party vendors will not be accepted; these resumes will be deleted and the candidates submitted will not be considered for employment.
If you have trouble applying for a position, please email ORNLRecruiting@ornl.gov.


ORNL is an equal opportunity employer. All qualified applicants, including individuals with disabilities and protected veterans, are encouraged to apply.  UT-Battelle is an E-Verify employer.Apply now 

Categories
Defenses

Dissertation Defense: A COMPREHENSIVE EVALUATION OF FEATURE-BASED MALICIOUS WEBSITE DETECTION.”

Author: John McGahagan

Advisory Committee:
Professor Michel Cukier, Chair
Professor Jennifer Golbeck, Dean’s Representative
Associate Professor Katrina Groth
Professor Jeffrey Herrmann
Professor Mohammad Modarres 

Date/Time: October 23rd 1pm-3pm ET

Abstract: Although the internet enables many important functions of modern life, it is also a ground for nefarious activity by malicious actors and cybercriminals. For example, malicious websites facilitate phishing attacks, malware infections, data theft, and disruption. A major component of cybersecurity is to detect and mitigate attacks enabled by malicious websites. Although prior researchers have presented promising results – specifically in the use of website features to detect malicious websites – malicious website detection continues to pose major challenges. This dissertation presents an investigation into feature-based malicious website detection. We conducted six studies on malicious website detection, with a focus on discovering new features for malicious website detection, challenging assumptions of features from prior research, comparing the importance of the features for malicious website detection, building and evaluating detection models over various scenarios, and evaluating malicious website detection models across different datasets and over time. We evaluated this approach on various datasets, including: a dataset composed of several threats from industry; a dataset derived from the Alexa top one million domains and supplemented with open source threat intelligence information; and a dataset consisting of websites gathered repeatedly over time. Results led us to postulate that new, unstudied, features could be incorporated to improve malicious website detection models, since, in many cases, models built with new features outperformed models built from features used in prior research and did so with fewer features. We also found that features discovered using feature selection could be applied to other datasets with minor adjustments. In addition: we demonstrated that the performance of detection models decreased over time; we measured the change of websites in relation to our detection model; and we demonstrated the benefit of re-training in various scenarios.