SiTime,the market leader in silicon MEMS timing, will host an internship open house on Thursday, March 16 at 4-6 pm in 1100 Victors Way suite 1150, Ann Arbor for students interested in MEMS, sensor and/or system design, modeling and testing. Please RSVP by March 15 to Jay Mitchell at jmitchell@sitime.com
Submit your application for an opportunity to learn the tools and techniques needed to carry out computational science on the world’s most powerful supercomputers. ATPESC participants will have access to DOE’s leadership-class systems at the ALCF, OLCF, and NERSC.
Program curriculum:
Renowned scientists and leading HPC experts will serve as lecturers and guide the hands-on training sessions. The core curriculum will cover:
Eligibility and application:
Doctoral students, postdocs, and computational scientists interested in attending ATPESC can review eligibility and application details on the website.
There are no fees to participate in ATPESC. Domestic airfare, meals, and lodging are also provided.
Applications for LANL’s X-Computational Physics (XCP) division’s summer 2023 workshop / internship programs are open now. Participants will receive a fellowship stipend, the amount to be determined based on your current academic rank. There are two programs, the computational physics workshop and the parallel computing workshop. Both programs are 10 weeks in duration and require US citizenship. Admissions are rolling, with a closing date of 1/16/2023 for the Computational physics workshop and 1/20/2023 for the parallel computing workshop.
The workshops are geared toward undergraduates and early graduate students, and either would be a great introduction to LANL, and aspects of XCP’s mission (there is significant lecture time built into the schedule of both). Please note that these are effectively XCP Divisions summer internship program if students don’t already have a direct hire mechanism worked out.
Computational Physics Summer Workshop 2023
Date: June 12 – August 18, 2023
Application deadline: January 16, 2023
Parallel Computing Summer Research Internship 2023
Date: June 12 – August 18, 2023
Application deadline: January 20, 2023
The Oden Institute for Computational Engineering and Sciences at UT Austin, Sandia National Laboratories (SNL), and Lawrence Livermore National Laboratory (LLNL) are partnering to host the 4th Rising Stars in Computational and Data Sciences, an intensive workshop for women graduate students and postdocs who are interested in pursuing academic and research careers.
Date: April 12-13, 2023
Place: Austin, TX University of Texas Oden Institute for Computational Engineering and Sciences
The Oden Institute is seeking nominations for outstanding candidates in their final year of PhD or within three years of having graduated. We will select approximately 30 women to come for two days of research presentations, poster sessions, and interactive discussions about academic and research careers, with financial support for travel provided.
The nomination consists of sending (1) a letter of nomination and (2) a copy of the nominee’s 2-page resume to Karissa Vail at karissa.vail@austin.utexas.edu. More information can be found at https://risingstars.oden.utexas.edu/
Please consider nominating one of your outstanding current/recent PhD students or postdocs.
Nominations are due: January 23, 2023.
Workshop theme
This week-long workshop will provide an introduction to the core theoretical and applied engineering topics of the DDE programe. Three sessions will be devoted to foundational techniques and methodologies including (i) reinforcement learning and control, (ii) uncertainty quantification and data assimilation, and (iii) model reduction. Another three sessions will be devoted to introductions to current and future challenges for data-driven engineering arising from (i) aeronautics, (ii) chemical and (iii) structural engineering. The tutorials will lay the foundation for the follow-up activities of the DDE programe and the deep-dive study periods in particular. The tutorial workshop is suited in particular for PhD students, postdocs and earlier career scientists from mathematics, statistics, computer science, and computational engineering.
Speakers
Date
January 16 – 20, 2023
Location
Cambridge, UK or Online (more information in the application section)
Application Deadline: 30 Sep 2022
XSEDE and the Pittsburgh Supercomputing Center are offering a two day Big Data and Machine Learning virtual workshop that will focus on topics including big data analytics and machine learning with Spark, and deep learning using Tensorflow.
When: Wed., April 6 @ 11:00 a.m. – 5:00 p.m. E.S.T. & Fri., April 8 @11:00 a.m. – 5:30 p.m. E.S.T.
Registration closes on April 4, 2022. Space is limited.
Wednesday, April 6
All times given are Eastern
11:00 Welcome
11:25 A Brief History of Big Data
12:20 Intro to Spark
1:00 Lunch break
2:00 More Spark and Exercises
3:00 Intro to Machine Learning
5:00 Adjourn
Friday, April 8
All times given are Eastern
11:00 Machine Learning: Recommender System with Spark
1:00 Lunch break
2:00 Deep Learning with Tensorflow
5:00 Tying it All Together
5:30 Adjourn
The Oden Institute for Computational Engineering and Sciences at UT Austin, Sandia National Laboratories (SNL), and Lawrence Livermore National Laboratory (LLNL) are partnering to host Rising Stars in Computational and Data Sciences, an intensive workshop for women graduate students and postdocs who are interested in pursuing academic and research careers.
The workshop will be held on April 20-21, 2022, in Albuquerque, NM.
The organizers are seeking nominations for outstanding candidates in their final year of PhD or within three years of having graduated. Approximately 30 women will be selected to come to Albuquerque for two days of research presentations, poster sessions, and interactive discussions about academic and research careers, with financial support for travel provided.
The nomination form requires (1) a letter of nomination and (2) a copy of the nominee’s resume.
Full details, including the nomination form and highlights from previous events can be found here.
Please consider nominating one of your outstanding current/recent PhD students or postdocs.
Nominations are due February 18, 2022.
U-M graduate students interested in computational and data sciences are invited to learn about joint programs that will prepare them for success in computationally intensive fields. The programs are organized by the Michigan Institute for Computational Discovery & Engineering, and the Michigan Institute for Data Science. Both institutes offer vast training and networking opportunities, including webinar series, symposia and student centered events.
Two sessions are scheduled
The sessions will address:
The Graduate Certificate in Computational Discovery and Engineering: trains students in computationally intensive research so they can excel in interdisciplinary HPC-focused research and product development environments.
The Graduate Certificate in Data Science: focuses on developing core proficiencies in data analytics: modeling, technology and practice.
The Graduate Certificate in Computational Neuroscience: provides training in interdisciplinary computational neuroscience to students in experimental neuroscience programs, and to students in quantitative science programs, such as physics, biophysics, mathematics and engineering.
The Ph.D. in Scientific Computing: open to all Ph.D. students who will make extensive use of large-scale computation, computational methods, or algorithms for advanced computer architectures in their studies. It is a joint degree program, with students earning a Ph.D. from their current departments, “… and Scientific Computing” — for example, “Ph.D. in Aerospace Engineering and Scientific Computing.”
“Community resilience is a manifestation of the human trait of adaptation. A resilient community is able to withstand and recover from hazardous events with minimal disruption to its way of life.”
Sherif El-Tawil
Antoine E. Naaman Collegiate Professor,
Department of Civil and Environmental Engineering
The combination of natural hazards, climate change, and the COVID-19 pandemic has demonstrated the importance of community resilience. Community resilience is a manifestation of the human trait of adaptation. A resilient community is able to withstand and recover from hazardous events with minimal disruption to its way of life. As humans, we seek to use our ability to engineer to adapt to the threat of natural hazards. Although achieving resilience is technically challenging and expensive, communities must strive to accomplish the highest level of resilience attainable with the engineering and financial resources available.
The science behind resilience engineering involves many disciplines, each dedicated to a subset of the overall problem. Complex issues lie at the intersection of these subsets, but interdisciplinary research is difficult to achieve because researchers in various disciplines frame problems and perform research from different perspectives and along distinct pathways. However, as computational models are well established in each discipline, computation is a natural language that links the disciplines together.
Last fall, the Michigan Institute for Computational Discovery and Engineering and the department of Civil and Environmental Engineering brought together established leaders and some of the most innovative rising scholars in the computational hazards research, to present and discuss different computational approaches used in modeling, assessing, and defining standards for community resilience. The speakers included representatives from leading research centers in the field: keynote speaker, Terri McAllister, from the National Institute of Standards and Technology (NIST); John van de Lindt (Colorado State University) co-director of the NIST-funded Center of Excellence (CoE) for Risk-Based Community Resilience Planning; Gregory Deierlein (Stanford University) from the SimCenter, which represents a consortium of universities on the U.S. West Coast; Sherif El-Tawil (University of Michigan) from ICoR, and Wael El-Dakhakhni (McMaster University) from INTERFACE. They were joined
by other leaders in the fields including Tasos Sextos from Bristol University, UK, Xinzheng Lu, head of the Institute of Disaster Prevention and Mitigation of Tsinghua University; Hiba Baroud from Vanderbilt University, and Seth Guikema from the University of Michigan. The speakers highlighted their Centers’ or research groups’ capabilities and contributions, then reconvened for a panel discussion to address questions from the audience of nearly 250 participants from 30 countries, across six continents. The event also included a hands-on workshop that highlighted the Simple Run-Time Infrastructure software toolkit (SRTI). The SRTI is a free, open-source solution developed at the University of Michigan. It enables researchers to connect computer programs and simulators written in different languages, share data during execution, and design hybrid systems using disparate simulator modules, with a primary goal of being user friendly. The applications within this workshop demonstrated how one tool can be used to bring together multiple computational dialects to create a single language in the context of natural hazards research. The SRTI software toolkit is a result of the work of Dr. Sherif El-Tawil’s research group at the University of Michigan, supported by the National Science Foundation’s Office of Advanced Cyberinfrastructure (OAC) under grant CRISP TYPE II – 1638186. (icor.engin.umich.edu).
The range of techniques and principles that were detailed at this workshop can be applied to the current COVID-19 crisis. The pandemic is a perfect example that demonstrates that investing in mitigating risk reduces the cost, both human and material, of a hazard, and that even hazards with such a low probability of occurrence require enough investment to make ourselves resilient to it. The pandemic also illustrates that computational hazards research is a rich field with many opportunities at the intersection of the various disciplines. One of the most interesting ideas there is to explore is how to fuse sensor data – from the field – with simulations data, to achieve models that can help predict in real time the effect of a natural hazard.
2020 SIAM Mini-Symposium in Applied Mathematics
The SIAM student chapter at the University of Michigan is hosting a student mini-symposium in applied mathematics on May 29, 2020. This event will allow students from different disciplines in the area to see what is being done in the field and promote interest in applied mathematics in general. This mini-symposium is open to all graduate students at the University of Michigan whose research is related to applied mathematics and/or scientific computing.
Time: Friday, May 29th, 10:00am – 4:00pm
Location: East Hall 3096, Department of Mathematics
Important Deadlines:
Deadline for submission of abstracts: April 14, 2020
Registration deadline: May 5, 2020
Registration is Open!
Registration to attend the 2020 SIAM Student Mini-Symposium in Applied Mathematics is now open. All are welcome to attend the conference, regardless of registration status, but lunch will be provided only for registered attendees.
To register please fill out the form provided by May 5, 2020.
The link for the mini-symposium can be found here: https://sites.google.com/view/siam-minisymposium-2020.
