Explore ARCExplore ARC

[SC2 Jobs] Paid summer internship with ARC-TS and Science Gateways Community Institute

By | SC2 jobs

Hands-on work experiences for undergraduate and graduate students

The Science Gateways Community Institute (SGCI) Workforce Development team is looking for a summer intern interested in developing their gateway development skills. Eligible applicants include graduate students majoring in computer science or computer engineering (or related fields) at any level and undergraduates majoring in computer science or computer engineering (or related fields) who have completed their junior year and who demonstrate strong programming and software engineering skills.

Location: University of Michigan at ARC-TS offices (Central Campus)
Stipend: $500/week
Contact: Brock Palen at brockp@umich.edu ASAP

Interns will be required to attend the Gateways 2018 conference, for which SGCI Workforce Development will provide funding. Attending PEARC18 is recommended, but not required. Funding will be provided by SGCI Workforce Development to interns who decide to attend.

More information at sciencegateways.org/engage/internships *Note that even though the website says the application window is closed, ARC-TS still has a position opened.

[SC2 Jobs] Postdoctoral position at the NIH/NIMH

By | SC2 jobs

We are seeking enthusiastic applicants for a Post-Doctoral Fellowship position to help with the collection and analysis of large brain-imaging datasets. The successful candidate will use state-of-the-art artificial intelligence methods, with the aim of better understanding psychiatric disorders in young people with mental illness, particularly anxiety and depression. Our goal is to understand better the causes and mechanisms of certain psychiatric disorders, improve their definition and classification, and ensure the best treatment can be offered to psychiatric patients.

The successful candidate will develop and apply deep learning algorithms to multi-modal imaging datasets that include MRI (functional, structural), EEG, MEG, and associated behavioral and clinical data. The methods developed by the successful candidate will be used to:

– Integrate these diverse sources of information.

– Inform the construction computational models in psychiatry.

– Test the validity of such models.


Candidates with a strong computational background (e.g. PhD in Engineering, Physics, Computer Science, Mathematics, Statistics, Computational Neuroscience, and related areas) who are interested in brain development and psychopathology, are particularly encouraged to apply. Requirements for this position include:

– Strong machine learning experience;

– Programming experience in Python (preferably), or in R/Matlab/Octave;

– Experience with open source machine learning libraries such as Scikit-learn, Theano, and/or Tensorflow;

– Excellent interpersonal and written (English) communication skills.

Background experience in psychiatry or knowledge of neuroimaging software are not required. However, the candidate will be expected to learn some of these topics as part of their role in our research group.

The successful candidate will work jointly with the laboratories of Drs Daniel Pine and Argyris Stringaris, and together with Dr Anderson Winkler, Staff Scientist. Please write to Drs Pine (pined@mail.nih.gov), Stringaris (argyris.stringaris@nih.gov) or Winkler (anderson.winkler@nih.gov) with your application and CV.

Job category

Postdoctoral position


ARC-TS begins work on new “Great Lakes” cluster to replace Flux

By | Flux, Happenings, HPC, News

Advanced Research Computing – Technology Services (ARC-TS) is starting the process of creating a new, campus-wide computing cluster, “Great Lakes,” that will serve the broad needs of researchers across the University. Over time, Great Lakes will replace Flux, the shared research computing cluster that currently serves over 300 research projects and 2,500 active users.

“Researchers will see improved performance, flexibility and reliability associated with newly purchased hardware, as well as changes in policies that will result in greater efficiencies and ease of use,” said Brock Palen, director of ARC-TS.

The Great Lakes cluster will be available to all researchers on campus for simulation, modeling, machine learning, data science, genomics, and more. The platform will provide a balanced combination of computing power, I/O performance, storage capability, and accelerators.

ARC-TS is in the process of procuring the cluster. Only minimal interruption to ongoing research is expected. A “Beta” cluster will be available to help researchers learn the new system before Great Lakes is deployed in the first half of 2019.

The Flux cluster is approximately 8 years old, although many of the individual nodes are newer. One of the benefits of replacing the cluster is to create a more homogeneous platform.

Based on extensive input from faculty and other stakeholders across campus, the new Great Lakes cluster will be designed to deliver similar services and capabilities as Flux, including the ability to accommodate faculty purchases of hardware, access to GPUs and large-memory nodes, and improved support for emerging uses such as machine learning and genomics. The cluster will consist of approximately 20,000 cores.

For more information, contact hpc-support@umich.edu, and see arc-ts.umich.edu/systems-services/greatlakes, where updates to the project will be posted.

Eric Parish, Aero Ph.D student, wins Von Neumann Fellowship from Sandia National Labs

By | Happenings, News, Research

Eric Parish

Eric Parish, who will graduate this spring with a Ph.D in Aerospace Engineering, is the 2018 recipient of the prestigious John von Neumann Postdoctoral Research Fellowship from Sandia National Laboratories (SNL). The highly competitive fellowship offers the opportunity to establish his own program at SNL to conduct innovative research in computational mathematics and scientific computing on advanced computing architectures.

Parish came to U-M from the University of Wyoming, and has developed innovative methodologies of computational math and physics with Prof. Karthik Duraisamy.

Parish said two of his accomplishments in his doctoral work have been developing data-driven solutions to computational physics problems using the NSF-funded ConFlux computing cluster, and bringing together ideas from statistical mechanics to develop efficient numerical solutions of complex partial differential equations.

“It was bridging a gap between communities,” he said of the latter effort.

“Eric came up with a particularly clever way of generalizing concepts from physics to develop a foundation to solve complex equations at a low cost in a mathematically rigorous fashion,” Duraisamy said. “He is one of the rare students who commands an exceptional grasp of applied mathematics, computing and physics, while being well-rounded in his organizational and communication skills. It has been a pleasure and a privilege to work with him.”

Parish said this research could eventually help usher the next generation of flight, for example, “hypersonic” aircraft that can travel at speeds of Mach 8-10. To help get there, his work moves the field toward a better understanding of the underlying physical phenomena via accurate numerical simulations.

At Sandia’s labs in Livermore, Calif., Parish said he plans to continue the work he started at U-M to develop “reduced order models”, which can process past simulation data to greatly reduce the computational cost of future simulations.

Parish said that conducting research at U-M, with the availability of high performance computing resources and a community of computational scientists to bounce ideas off of, helped push his research to a higher level.

“Within Aero, there are five or six very strong computational groups, which really helps me understand the fundamental aspects of what we’re doing, and what the addition of my small little delta means,” he said. “It’s very exciting to do computational research in that environment; it motivates me to come up with better code.”

In 2016, Parish received a $4,000 fellowship from the Michigan Institute for Computational Discovery and Engineering (MICDE). He used some of the funds to attend the International Workshop on Variational Multiscale Methods in Spain last year, where he met a few dozen people from around the world working on similar problems.

“It was fantastic to network and learn from them,” he said.

Parish grew up in Laramie, Wyo., before attending the University of Wyoming, where he played Division 1 golf. He said there was a small but active computational science community at U-W.

“For its size, there was a lot of good computational stuff there,” he said, adding that 10 years ago he would never have predicted the current direction of his career.

Golf played a significant role in his development as well, Parish said: “Being a successful student-athlete takes an extraordinary amount of work. The successes and failures I had … played an integral part in the development of my work ethic, time management skills, mental attitude, and overall growth as a person…I believe that the experience I gained as a student-athlete gave me a unique perspective and skill set that I was able to use to my advantage.”

As far as his future goes after Sandia, Parish said he plans to either continue in the national lab environment or to explore faculty positions so that he can teach and motivate students as his professors at Wyoming and Michigan did for him.

“I’m grateful for everyone’s help,” he said. “The doors that Michigan can open and the quality of people here are very apparent.”

A simulation of magnetohydrodynamic turbulence done on the ConFlux cluster with roughly 1 billion degree of freedom computation generating about 4TB of data.

[SC2 Jobs] Schlumberger Summer Internship: 3D EM solver optimization

By | SC2 jobs

The Mathematics and Modeling Department at Schlumberger Doll Research (SDR) is investigating the development of state of the art data science methods to measurement data pertaining to complex problems in the development of oilfields. The data are acquired inside wells using multi-physics measurements ranging from electromagnetic (optical, NMR, low-frequency EM, resisitivity) to acoustical (ultrasonic, sonic, seismic).

Schlumberger-Doll Research (SDR) is the prime corporate research center for Schlumberger, the world’s leading supplier of technology, integrated project management and information solutions to customers working in the oil and gas industry worldwide. SDR hosts more than 110 scientists working in various fields including geophysical measurements, geosciences, and computational sciences. SDR is located in Cambridge at minute-walking from many landmark buildings such as MIT CSAIL Stata center. Several dozen interns are hosted each summer at SDR. Previous interns have highlighted the working environment, camaraderie, diversity in expertise and domains of interest, learning about new technical challenges, the cafeteria and the outside activities offered in Cambridge as prime elements they enjoyed during their stay at SDR. Internships have often lead to presentations, conference and journal publications as well as patent applications.

Applicants should send a brief letter of intent and resume via E-mail to SDRJobs@slb.com with the reference MM-SZ.

Schlumberger is an equal opportunity employer and is committed to the diversity of its workforce.

Responsibilities and Qualifications:
The intern will work within a multi-disciplinary team to adapt and implement new 3D low frequency finitie-difference EM solver for 3D oil reservoir characterization and real-time reservoir mapping and navigation.

The 3D solver needs to be paralllelized and optimized for performance on multicore Windows and Linux platforms and on GPUs. The will be interfaced to imaging inversions.

The successful summer intern has a stronger background in HPC, fortran, and experience with optimizing computational algorithms. Familiarity with PDE solvers and computational electromagentics is preferred.

Advanced undergraduate or graduate (MS/PhD) student in Applied math, CE, CS, EE, ME, or related fields with courses, and preferably thesis work, related to the subject domain.

Job category

Summer Internship: 3D EM solver optimization


Schlumberger Doll Research (SDR), Cambridge, MA, USA