HPC training workshops begin Tuesday, Feb. 13

By | Educational, Events, General Interest, Happenings, HPC, News

series of training workshops in high performance computing will be held Feb. 12 through March 6, 2018, presented by CSCAR in conjunction with Advanced Research Computing – Technology Services (ARC-TS).

Introduction to the Linux command Line
This course will familiarize the student with the basics of accessing and interacting with Linux computers using the GNU/Linux operating system’s Bash shell, also known as the “command line.”
Location: East Hall, Room B254, 530 Church St.
Dates: (Please sign up for only one)
• Tuesday, Feb. 13, 1 – 4 p.m. (full descriptionregistration)
• Friday, Feb. 16, 9 a.m. – noon (full description | registration)

Introduction to the Flux cluster and batch computing
This workshop will provide a brief overview of the components of the Flux cluster, including the resource manager and scheduler, and will offer students hands-on experience.
Location: East Hall, Room B254, 530 Church St.
Dates: (Please sign up for only one)
• Monday, Feb. 19, 1 – 4 p.m. (full description | registration)
• Tuesday, March 6, 1 – 4 p.m. (full description | registration)

Advanced batch computing on the Flux cluster
This course will cover advanced areas of cluster computing on the Flux cluster, including common parallel programming models, dependent and array scheduling, and a brief introduction to scientific computing with Python, among other topics.
Location: East Hall, Room B250, 530 Church St.
Dates: (Please sign up for only one)
• Wednesday, Feb. 21, 1 – 5 p.m. (full description | registration)
• Friday, Feb. 23, 1 – 5 p.m. (full description | registration)

Hadoop and Spark workshop
Learn how to process large amounts (up to terabytes) of data using SQL and/or simple programming models available in Python, R, Scala, and Java.
Location: East Hall, Room B250, 530 Church St.
Dates: (Please sign up for only one)
• Thursday, Feb. 22, 1 – 5 p.m. (full description | registration)

2016-2017 MICDE Research Snapshot

By | Research

2016-2017 has been a year of sustained growth for MICDE’s research portfolio. The number of faculty affiliated with the institute stands at 130, spanning 30 departments and eight schools and colleges. The Center for Scientific Software Infrastructure was established to bring together the U-M community engaged in developing open scientific software. It will focus on establishing best practices for developing, disseminating and documenting scientific software in the public domain. Led by Prof. Emanuel Gull (Physics), the Center aims to provide training and support for researchers that are ready to transform their research codes into well-engineered software. It offers grant support in the form of programmers, consultants, and administrative assistance. It includes a portal to share your code with the research community at large.

MICDE’s two established centers, the Center for Network and Storage-Enabled Collaborative Computational Science (CNSECCS) and the Center for Data-Driven Computational Physics (DaCoP), each held their first symposium, showcasing their first year of research activities. This included evidence of the growing reach of OSiRIS, the open framework for storage, computation and collaboration against big scientific data, and the first results from ConFlux, U-M’s groundbreaking computing cluster for data-driven computational physics. These results have been presented at several conferences, and are appearing in the leading computational journals.

Vorticity field at a late time in the evolution of an elliptic vortex patch computed by a Lagrangian particle method with remeshing and treecode-accelerated evaluation of the Biot-Savart integral. (source: Ling Xu)

MICDE also funded its first round of Catalyst Grants, that are supporting four innovative computational science research projects. Research funded by the Catalyst Grants is breaking new ground, while helping define the future of computational science. This research consists of:

  • studies of the neuronal dynamics of learning and memory formation;
  • new algorithms for the complex, nonlinear dynamics of power grids;
  • novel integral equations methods using recent advances in numerical analysis;  
  • and probabilistic computational frameworks for rare but often catastrophic events.

The past academic year MICDE hosted 14 external speakers with backgrounds and research concentrations that span the breadth of computational science of today and the future. The series culminated in MICDE’s annual symposium: “The New Era of Data-Enabled Computational Science,” which featured talks by worldwide leaders in computational science, including U-M faculty. The symposium included a student poster competition with over 50 entries.

Dr. Ann Almgren from the Lawrence Livermore National Lab speaking about Next Generation AMR, part of the 2016-2017 MICDE Seminar Series

MICDE faculty are committed to growing the already strong U-M community of computational scientists. Over the past year, as before, we have organized a number of workshops to foster collaboration and put together interdisciplinary teams in response to funding calls from federal agencies and foundations.   MICDE offers faculty teams institutional support and direct links to our excellent educational programs as well as cyberinfrastructure, all of which strengthen faculty proposals. With the backing of our parent unit, Advanced Research Computing (ARC), and its technical and consulting services (ARC-Technology Services, and Consulting for Statistics, Computing and Analytics Research), this effort has raised over $22M in external funding over the past 2 years. This includes support from federal agencies (NSF, NIH, and DOD), as well as from industry.  We also work with the academic units at U-M to identify compelling new areas for recruiting the type of faculty members who will drive computational science in the future.

 

2016-2017 Education Snapshot

By | Educational, General Interest, News

Over the past year, MICDE’s educational programs and activities have experienced tremendous growth. The Graduate Certificate in Computational Discovery and Engineering currently has 50 students enrolled, spanning 19 departments from 5 different schools and colleges. Sixteen students graduated within the last academic year, and 44 have graduated since the Graduate Certificate was established in 2013. Even further, the number of women in the program went from zero in 2014 to 15 currently enrolled.

The Ph.D. in Scientific Computing has experienced extraordinary growth, with 74 students enrolled from 20 departments, and four schools or colleges. We added a section to our web site with both programs’ alumni information.

We are working to broaden as well as to deepen the activities and resources available to students in both programs. Twenty MICDE fellowships were awarded this academic year to students in our programs. We continued to sponsor student software teams at competitions, as well as individual students presenting their work at leading conferences. On-campus, MICDE student activities include networking lunches, and the Scientific Computing Student Club (SC2). On the programmatic front, our non-engineering students now have access to a CAEN account that gives them more options to connect and use U-M High Performance Computing resources. Relevant grant opportunities for students are tracked and updated in MICDE’s grant webpage

2016-2017 MICDE Fellow Yuxi Chen (ClaSp) presenting his work at the MICDE Annual Symposium

Several educational projects and initiatives are afoot at MICDE, including a Massively Open Online Class (MOOC) in Computational Thinking targeting both high school students and their teachers. This MOOC aims to introduce learners to algorithmic approaches to problems. This initiative is being developed in collaboration with the School of Education, the office of Academic Innovation, and with input from a number of high schools in the Detroit Metropolitan Area.The two new courses launched by MICDE faculty last year, Methods and Practices of Scientific Computing, and Data-Driven Analysis and Modeling of Complex Systems, were successful in their first offerings during the 2016-2017 academic year, and are being offered again in 2017-2018. Other teams of MICDE faculty are at work across campus to develop new courses in computational science.

2016-2017 Outreach and Industrial Engagement Snapshot

By | General Interest

2017 miRcore’s GIDAS Biotechnology Summer Camp participants

Community Outreach

MICDE remains committed to advancing the understanding of science in general, and computational science in particular, in the community. To this end we have continued our support of internal and external organizations. Externally, our ongoing support of the non profit science outreach group, miRcore, included running MICDE sponsored compute cycles on Flux for high school students participating in miRcore’s computational biology summer camps through their student network called GIDAS. We also continued to support the undergraduate Biosoftware Team that has competed in the International Genetically Engineered Machine (iGEM) year competition for the past five years. The team participates in the software track aimed for computer scientists and developers to nurture their knowledge of biology, and for computational biologists, bioinformaticians and biologists to enhance their aptitude for building software. Over the past couple of years, the team has been developing ProtoCat, a software developed to address the issue of reproducibility in synthetic biology. It is a collaborative platform on which researchers share their experiment protocols and users can customize them to meet their own needs. For the third year in a row, the team returned with a gold medal.

2017 BioSoftware Team

Internally, less than two years since its inception, the Scientific Computing Student Club (SC2) has established several activities that complement the formal training in computational science available at U-M, including through MICDE’s PhD in Scientific Computing, and Graduate Certificate in Computational Discovery and Engineering. Over the past year, the SC2 had his own invited speakers, organized tours to the Flux facility and the U-M 3D Lab, organized the first Visualization Challenge, co-sponsored by NVIDIA, and just recently added a section on its web page for academic and non-academic job opportunities. During the 2017 Fall Term, SC2 students ran a weekly Machine Learning Collaborative Workshop, and the group is planning a hands-on series on code parallelization.

Industrial Engagement

We continue working towards increasing our engagement with industry. Over the last two years, in addition to NVIDIA, MICDE has established partnerships with IBM, through the joint design and development of our computer cluster, ConFlux, and with Toyota Research Institute, through a funded project on scientific software for materials research. We are now working in partnership with the U-M Business Engagement Center to create an MICDE industrial affiliates program, which will provide many additional avenues for interaction between our students or faculty and industry.

 

 

 

[SC2 Jobs] Machine Learning Scientist for Toyota Research Institute of North America

By | SC2 jobs

Toyota Research Institute of North America, located in Ann Arbor, Michigan, is seeking a machine learning scientist to support the in-house research activities. This individual will join a team responsible to develop state-of-art methodologies for material informatics. The position requires staying abreast of emerging field of machine learning, performing original research, publishing/presenting results, involve in collaborative research. Candidate must be able to work effectively with a diverse group of scientists.
The position is subject to annually contract renewal.

Key Responsibilities:
• Develop machine learning models to deal with problems/challenges in material informatics;
• Establish tools to collect and structure materials data and harvest valuable information
subsequently;
• Perform text mining from scientific literatures and internal technical documents;
• Frequently communicate with materials scientist within the organization;
• Effectively respond to the challenges emerging in materials project;
• Regularly report and present to the research team and managements;

Minimum Requirements:
• MS or above degree in Computer Science, Statistics or related technical field or equivalent
practical experience;
• Strength with machine learning and text mining techniques;
• Fluency in programming languages (Python, C/C++, Java);
• Hands-on experience with statistical software (R, SAS, Matlab, Python);
• Strong verbal and written communication skills;
• Self-motivated, intelligent individual with initiative and drive for overcoming technical
challenges;

Preferred Requirements:
• Experience with deep learning techniques;
• Experience in projects related to materials science, chemistry and physics;
• Established capability in scientific writing and presentation;

 

The applicant should send the resume to chen.ling@toyota.com before February 9, 2018.

Job category

Machine Learning Scientist

Location

Ann Arbor, MI

Application deadline

February 9, 2018

[SC2 Jobs] Stephen Timoshenko Distinguished Postdoctoral Fellowship at Stanford University

By | SC2 jobs

 

 

 

 

The Mechanics and Computation Group (Department of Mechanical Engineering) at Stanford University is seeking applicants for the “Stephen Timoshenko Distinguished Postdoctoral Fellowship.” This appointment is for a term of two years, beginning in September 2018.

The Stephen Timoshenko Distinguished Postdoctoral Fellow will be given the opportunity to pursue independent research in the general area of solid mechanics, as well as to contribute to ongoing research in the Mechanics and Computation Group. Research activities should be in the field of solid mechanics interpreted broadly, including areas such as additive manufacturing, micro- and nano-mechanics, bio-mechanics, and related research directions such as applications of machine learning. Candidates will be given opportunities to develop their teaching experience by designing and teaching a class in the mechanics curriculum. This position might be of particular interest to candidates who are seeking an academic career.

Candidates are expected to show outstanding promise in research, as well as strong interest and ability in teaching. They must have received a Ph.D. prior to the start of the appointment, but not before 2016. Applicants should send a cover letter (one page); a curriculum vitae; a list of publications; brief statements of proposed research (up to three pages) and teaching (one page); the names and contact information of three recommendation letter writers.

For full consideration, applications must be completed no later than 11PM PST, Sunday March 4, 2018. However, applications will continue to be accepted until the position is filled.

Stanford is an equal opportunity employer and all qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other characteristic protected by law. Stanford also welcomes applications from others who would bring additional dimensions to the University’s research and teaching missions.

Please send your application by email to:
Norma Costello, normac@stanford.edu, 650 723-4133
Email subject: Stephen Timoshenko Distinguished Postdoctoral Fellow search
All documents attached to the email should be PDF (Portable Document Format).

For updates see https://mechanics.stanford.edu/hiring.

 

Job category

Postdoctoral Fellowship

Location

Stanford University

Application deadline

No later than 11 p.m., Sunday, March 4, 2018

U-M fosters thriving artificial intelligence and machine learning research

By | General Interest, HPC, News, Research

Research using machine learning and artificial intelligence — tools that allow computers to learn about and predict outcomes from massive datasets — has been booming at the University of Michigan. The potential societal benefits being explored on campus are numerous, from on-demand transportation systems to self-driving vehicles to individualized medical treatments to improved battery capabilities.

The ability of computers and machines generally to learn from their environments is having transformative effects on a host of industries — including finance, healthcare, manufacturing, and transportation — and could have an economic impact of $15 trillion globally according to one estimate.

But as these methods become more accurate and refined, and as the datasets needed become bigger and bigger, keeping up with the latest developments and identifying and securing the necessary resources — whether that means computing power, data storage services, or software development — can be complicated and time-consuming. And that’s not to mention complying with privacy regulations when medical data is involved.

“Machine learning tools have gotten a lot better in the last 10 years,” said Matthew Johnson-Roberson, Assistant Professor of Engineering in the Department of Naval Architecture & Marine Engineering and the Department of Electrical Engineering and Computer Science. “The field is changing now at such a rapid pace compared to what it used to be. It takes a lot of time and energy to stay current.”

Diagram representing the knowledge graph of an artificial intelligence system, courtesy of Jason Mars, assistant professor, Electrical Engineering and Computer Science, U-M

Johnson-Roberson’s research is focused on getting computers and robots to better recognize and adapt to the world, whether in driverless cars or deep-sea mapping robots.

“The goal in general is to enable robots to operate in more challenging environments with high levels of reliability,” he said.

Johnson-Roberson said that U-M has many of the crucial ingredients for success in this area — a deep pool of talented researchers across many disciplines ready to collaborate, flexible and personalized support, and the availability of computing resources that can handle storing the large datasets and heavy computing load necessary for machine learning.

“The people is one of the reasons I came here,” he said. “There’s a broad and diverse set of talented researchers across the university, and I can interface with lots of other domains, whether it’s the environment, health care, transportation or energy.”

“Access to high powered computing is critical for the computing-intensive tasks, and being able to leverage that is important,” he continued. “One of the challenges is the data. A major driver in machine learning is data, and as the datasets get more and more voluminous, so does the storage needs.”

Yuekai Sun, an assistant professor in the Statistics Department, develops algorithms and other computational tools to help researchers analyze large datasets, for example, in natural language processing. He agreed that being able to work with scientists from many different disciplines is crucial to his research.

“I certainly find the size of Michigan and the inherent diversity that comes with it very stimulating,” he said. “Having people around who are actually working in these application areas helps guide the direction and the questions that you ask.”

Sun is also working on analyzing the potential discriminatory effects of algorithms used in decisions like whether to give someone a loan or to grant prisoners parole.

“If you use machine learning, how do you hold an algorithm or the people who apply it accountable? What does it mean for an algorithm to be fair?” he said. “Can you check whether this notion of non-discrimination is satisfied?”

Jason Mars, an assistant professor in the Electrical Engineering and Computer Science department and co-founder of a successful spinoff called Clinc, is applying artificial intelligence to driverless car technology and a mobile banking app that has been adopted by several large financial institutions. The app, called Finie, provides a much more conversational interface between users and their financial information than other apps in the field.

“There is going to be an expansion of the number of problems solved and number of contributions that are AI-based,” Mars said. He predicted that more researchers at U-M will begin exploring AI and ML as they understand the potential.

“It’s going to require having the right partner, the right experts, the right infrastructure, and the best practices of how to use them,” he said.

He added that U-M does a “phenomenal job” in supporting researchers conducting AI and ML research.

“The level of support and service is awesome here,” he said. “Not to mention that the infrastructure is state of the art. We stay relevant to the best techniques and practices out there.”

Advanced Research Computing at U-M, in part through resources from the university-wide Data Science Initiative, provides computing infrastructure, consulting expertise, and support for interdisciplinary research projects to help scientists conducting artificial intelligence and machine learning research.

For example, Consulting for Statistics, Computing and Analytics Research, an ARC unit, has several consultants on staff with expertise in machine learning and predictive analysis with large, complex, and heterogeneous data. CSCAR recently expanded capacity to support very large-scale machine learning using tools such as Google’s TensorFlow.

CSCAR consultants are available by appointment or on a drop-in basis, free of charge. See cscar.research.umich.edu or email cscar@umich.edu for more information.

CSCAR also provides workshops on topics in machine learning and other areas of data science, including sessions on Machine Learning in Python, and an upcoming workshop in March titled “Machine Learning, Concepts and Applications.”

The computing resources available to machine learning and artificial intelligence researchers are significant and diverse. Along with the campus-wide high performance computing cluster known as Flux, the recently announced Big Data cluster Cavium ThunderX will give researchers a powerful new platform for hosting artificial intelligence and machine learning work. Both clusters are provided by Advanced Research Computing – Technology Services (ARC-TS).

All allocations on ARC-TS clusters include access to software packages that support AI/ML research, including TensorFlow, Torch, and Spark ML, among others.

ARC-TS also operates the Yottabyte Research Cloud (YBRC), a customizable computing platform that recently gained the capacity to host and analyze data governed by the HIPAA federal privacy law.

Also, the Michigan Institute for Data Science (MIDAS) (also a unit of ARC) has supported several AI/ML projects through its Challenge Initiative program, which has awarded more than $10 million in research support since 2015.

For example, the Analytics for Learners as People project is using sensor-based machine learning tools to translate data on academic performance, social media, and survey data into attributes that will form student profiles. Those profiles will help link academic performance and mental health with the personal attributes of students, including values, beliefs, interests, behaviors, background, and emotional state.

Another example is the Reinventing Public Urban Transportation and Mobility project, which is using predictive models based on machine learning to develop on-demand, multi-modal transportation systems for urban areas.

In addition, MIDAS supports student groups involved in this type of research such as the Michigan Student Artificial Intelligence Lab (MSAIL) and the Michigan Data Science Team (MDST).

(A version of this piece appeared in the University Record.)

[SC2 Jobs] Postdoctoral and senior fellowships at the NASA Postdoctoral Program

By | SC2 jobs

The NASA Posdoctoral Program provides fellowships to conduct cutting-edge research at NASA Centers and NASA-affiliated research institutes, and is offering Postdoctoral and Senior Fellowship positions.

Research areas include aeronautics and engineering, astrobiology, astrophysics, biological sciences, cosmochemistry, earth science, heliophysics science, planetary science, technology development, and science management.

Appointments up to three years, and stipends begin at $53,500 increasing depending on locality and seniority. $8,000 is granted for support of professional travel per year. Health insurance and relocation assistance available.

Applications are due by March 1, July 1, November 1, 2018. Please see npp.usra.edu for more information. Women, minorities, and members of underrepresented communities are encouraged to apply.

Job category

Postdoctoral Fellow, Senior Fellowship

Location

N/A

Application deadline

March 1, July 1, November 1, 2018

[SC2 Jobs] Assistant Professor at the University of Nebraska, Lincoln

By | SC2 jobs

The Department of Mechanical & Materials Engineering at the University of Nebraska-Lincoln (http://mme.unl.edu) invites applications for two tenure-track faculty positions at the Assistant Professor level in the areas of (1) dynamics, systems, and design, or (2) thermal/fluids sciences and energy  conversion. Successful candidates are expected to develop an externally funded research program in emerging areas which may include: robotics and automation, micro/nanoscale thermal/fluids, optimal design, additive manufacturing, and micro/nanotechnology device development. The successful candidates will contribute to the undergraduate and graduate academic programs within the department and demonstrate a commitment to excellence in both teaching and research.

Applicants are expected to have a Ph.D. or equivalent in mechanical engineering or a closely related field. Applicants should have a record of strong scholarly achievement and a demonstrated commitment to excellence in undergraduate and graduate education with the potential to establish a strong externally-funded research program.

 

 

Please see https://employment.unl.edu/postings/56098 for more information on how to apply.

Job category

Assistant Professor

Location

Lincoln, Nebraska

 

[SC2 Jobs] Postdoc Research Associate at the Scientific Computation Research Center, Rensselaer Polytechnic Institute

By | SC2 jobs

The Scientific Computation Research Center  (www.scorec.rpi.edu) at Rensselaer is seeking highly qualified post-doctoral research associates to develop parallel adaptive unstructured mesh technologies that will be applied in multiple areas of application including fusion modeling, computational fluid dynamics and others.

Applicants are expected to have a PhD in Engineering, Applied Mathematics,  Computer/Computational Science, or related discipline. Applicants should have expertise in a subset of the areas listed and be interested in working closely with others that provide expertise in the other areas: Unstructured meshing generation/adaptation technologies, development and optimization of parallel Particle in Cell methods (PIC), development of parallel unstructured simulation technologies, experience in parallel programming and high performance computing, good knowledge of FORTRAN, C and/or C++ programming languages and GNU/Linux operating system is required, and knowledge of modern software engineering tools will be considered favorably.

Please see http://www.usacm.org/jobs/129 for more information on how to apply.

Job category

Postdoc Research Associate

Location

Troy, NY