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New course for fall 2018: On-Ramp to Data Science for Chemical Engineers

By | Educational, General Interest, Happenings, News

Description: Engineers are encountering and generating a ever-growing body of data and recognizing the utility of applying data science (DataSci) approaches to extract knowledge from that data. A common barrier to learning DataSci is the stack of prerequisite courses that cannot fit into the typical engineering student schedule. This class will remove this barrier by, in one semester, covering essential foundational concepts that are not part of many engineering disciplines’ core curricula. These include: good programming practices, data structures, linear algebra, numerical methods, algorithms, probability, and statistics. The class’s focus will be on how these topics relate to data science and to provide context for further self-study.

Eligibility: College of Engineering students, pending instructor approval.

More information: http://myumi.ch/LzqPq

Instructor: Heather Mayes, Assistant Professor, Chemical Engineering, hbmayes@umich.edu.

University of Michigan awarded Women in High Performance Computing chapter

By | General Interest, News

The University of Michigan has been recognized as one of the first Chapters in the new Women in High Performance Computing (WHPC) Pilot Program.

“The WHPC Chapter Pilot will enable us to reach an ever-increasing community of women, provide these women with the networks that we recognize are essential for them excelling in their career, and retaining them in the workforce.” says Dr. Sharon Broude Geva, WHPC’s Director of Chapters and Director of Advanced Research Computing (ARC) at the University of Michigan (U-M). “At the same time, we envisage that the new Chapters will be able to tailor their activities to the needs of their local community, as we know that there is no ‘one size fits all’ solution to diversity.”

“At WHPC we are delighted to be accepting the University of Michigan as a Chapter under the pilot program, and working with them to build a sustainable solution to diversifying the international HPC landscape” said Dr. Toni Collis, Chair and co-founder of WHPC, and Chief Business Development Officer at Appentra Solutions.

The process of selecting organizations to participate in the program accounted for potential conflicts of interest; Geva did not vote on U-M’s application.

About Women in High Performance Computing (WHPC) and the Chapters and Affiliates Pilot Program

Women in High Performance Computing (WHPC) was created with the vision to encourage women to participate in the HPC community by providing fellowship, education, and support to women and the organizations that employ them. Through collaboration and networking, WHPC strives to bring together women in HPC and technical computing while encouraging women to engage in outreach activities and improve the visibility of inspirational role models.

WHPC has launched a pilot program for groups to become Affiliates or Chapters. The program will share the knowledge and expertise of WHPC as well as help to tailor activities and develop diversity and inclusion goals suitable to the needs of local HPC communities. During the pilot, WHPC will work with the Chapters and Affiliates to support and promote the work of women in their organizations, develop crucial role models, and assist employers in the recruitment and retention of a diverse and inclusive HPC workforce.

WHPC is stewarded by EPCC at the University of Edinburgh. For more information visit http://www.womeninhpc.org.  

For more information on the U-M chapter, contact Dr. Geva at sgeva@umich.edu.

[SC2 Jobs] The Data Incubator Fellowship Program

By | SC2 jobs

Job Description

The Data Incubator is an intensive 8 week fellowship that prepares masters students, PhDs, and postdocs in STEM and social science fields seeking industry careers as data scientists. The program is free for Fellows and supported by sponsorships from hundreds of employers across multiple industries. In response to the overwhelming interest in our earlier sessions, we will be holding another fellowship.

Requirements

Anyone who has already obtained a masters or PhD degree or who is within one year of graduating with a masters or PhD is welcome to apply. Applications from international students are welcome. Everyone else is encouraged to sign-up for a future session.

Location

In addition to the below in-person locations, we will have a remote online session:

  • New York City
  • San Francisco Bay Area
  • Boston
  • Washington, DC.

Dates

All sections will be from 2018-09-10 to 2018-11-02.

Application Link

https://www.thedataincubator.com/fellowship.html#apply?ref=wcG93ZWxsQHVtaWNoLmVkdQo=

 

Data Science in 30 minutes

Learn how to build a data-science project in our upcoming free Data Science in 30-minutes webcast. Signup soon as space is limited.

Learn More

You can learn about our fellows at The New York Times, LinkedIn, Amazon, Capital One, or Palantir. To read about our latest fellow alumni, check out our blog. To learn more about The Data Incubator, check us out on Venture Beat, The Next Web, or Harvard Business Review.

MICDE awards seven Catalyst Grants

By | General Interest, Happenings, News, Research

The Michigan Institute for Computational Discovery and Engineering has awarded its second round of Catalyst Grants, providing between $80,000 and $90,000 each to seven innovative projects in computational science. The proposals were judged on novelty, likelihood of success at catalyzing larger programs and potential to leverage ARC’s computing resources.

The funded projects are:

Title: Exploring Quantum Embedding Methods for Quantum Computing
Researchers: Emanuel Gull, Physics; Dominika Zgid, Chemistry.
Description: The research team will design quantum embedding algorithms that can be early adopters of quantum computers on development of advanced materials for possible applications in modern batteries, next-generation oxide electronics, or high-temperature superconducting power cables.

Title: Teaching autonomous soft machines to swim
Researchers: Silas Alben, Mathematics; Robert Deegan, Physics; Alex Gorodetsky, Aerospace Engineering
Description: Self-oscillating gels are polymeric materials that change shape, driven by chemical reactions occurring entirely within the gel. The research team will develop a computational and machine learning program to discover how to configure self-oscillating gels so that they undergo deformations that result in swimming. The long term goal is to develop a general framework for controlling autonomous soft machines.

Title: Urban Flood Modeling at “Human Action” Scale: Harnessing the Power of Reduced-Order Approaches and Uncertainty Quantification
Researchers: Valeriy Ivanov, Civil and Environmental Engineering; Nikolaos Katopodes, Civil and Environmental Engineering; Darren McKague Climate and Space Sciences and Engineering; Khachik Sargsyan, Sandia National Labs.
Description: The research team will demonstrate urban flood monitoring and prediction capabilities using NASA Cyclone Global Navigation Satellite System (CYGNSS) data and relying on state-of-the-science uncertainty quantification tools in a proof-of-concept urban flooding problem of high complexity.

Title: Advancing the Computational Frontiers of Solution-Adaptive, Scale-Aware Climate Models
Researchers: Christiane Jablonowski, Climate and Space Sciences and Engineering; Hans Johansen, Lawrence Berkeley National Lab.
Description: Researchers will further develop a 3-D mesh adaptation model for climate modeling, allowing computational resources to be focused on phenomena of interest such as tropical cyclones or other extreme weather events. The project will also introduce data-driven machine learning paradigms into modeling of clouds and precipitation.

Title: Deciphering the meaning of human brain rhythms using novel algorithms and massive, rare datasets
Researchers: Omar Ahmed, Psychology, Neuroscience and Biomedical Engineering
Description: The team will develop a set of algorithms for use on high performance computers to analyze de-identified brain data from patients in order to better understand what electrical oscillations tell us about rapidly changing behavioral and pathological brain states.

Title: Embedded Machine Learning Systems To Sense and Understand Pollinator Behavior
Researchers: Robert Dick, Electrical Engineering and Computer Science; Fernanda Valdovinos Ecology and Evolutionary Biology, Center for Complex Systems; Paul Glaum, Ecology and Evolutionary Biology.
Description: To understand the mechanisms driving the population dynamics of pollinators, the research team will develop technologies for deeply embedded hardware/software learning systems capable of remote, long term, autonomous operation; and will analyze the resulting new data to better understand pollinator activity.

Title: Deep Learning for Phylogenetic Inference
Researchers: Jianzhi Zhang, Ecology and Evolutionary Biology; Yuanfang Guan, Computational Medicine and Bioinformatics.
Description: The research team will use deep neural networks to infer molecular phylogenies and extract phylogenetically useful patterns from amino acid or nucleotide sequences, which will help understand evolutionary mechanisms and build evolutionary models for a variety of analyses.

For more on the Catalyst Grants, see http://micde.umich.edu/catalyst/.

CASC image competition open for submissions

By | General Interest, Happenings, News

The image competition for the Coalition for Academic Scientific Computation (CASC) 2019 annual brochure is now open. Winning images will be featured in the brochure, which is distributed to industry, government and academia. An image from U-M Aerospace Engineering Professor Joaquim Martins was on the cover of the 2016 edition, and several U-M investigators have had their work featured in the brochure in other years.

Images will be judged on the following criteria:

  • Illustrative of research underway at the center submitting the proposed images
  • Focus on research that offers a broad representation of what CASC members have undertaken
  • Timeliness of visualization relative to events currently in the news
  • Exhibits intellectual merit
  • Provides scientific, cultural, economic impact
  • Compelling, visually interesting, lively, colorful images in a  high-resolution format

Please send potential submissions to Dan Meisler, ARC Communications Manager, at dmeisler@umich.edu. The deadline is June 11, 2018.

[SC2 Jobs] Scientist for the development of VASP

By | SC2 jobs

Job Description

The Vienna ab-initio Simulation Package (VASP) group seeks one scientist for the development of the software package VASP . VASP is a de facto standard for the simulation of condensed matter systems using the Schroedinger equation. A very exciting and lively working environment with many collaborative research projects involving theory as well as experimental groups is offered. The work will cover VASP software maintenance and support, optimization of the existing codes for latest high performance computer architectures (e.g. Intel Xeon, Nvidia GPU), cutting edge theory developments as enumerated below, as well as co-development of workflow tools (ASE, AiiDA, pymatgen, etc.)

Requirements

  • PhD in physics or chemistry
  • Excellent record in any area of computational solid state physics or chemistry. The areas of expertise can include – but are not restricted to – density functional theory, many-body Green’s function techniques, quantum field theoretical methods, quantum chemistry methods, or modeling of condensed matter systems.
  • Candidates with proven expertise in developing new computational methods and adapting them to high performance computers will be given preference.
  • Prior knowledge of VASP is advantageously but not strictly required.

Location

Vienna, Austria

To apply:

i) CV including full academic record,
ii) list of publications and talks including two reprints representative of previous research,
iii) expression of interest concerning research area(s) and previous expertise (one page).

Applications need to be sent to georg.kresse_at_univie.ac.at (topic: Scientific software developer).

Selection of candidates will start immediately and continue until the  positions are filled. The contract will be for one year initially, with the  possibility for a permanent contract after positive evaluation.

 

Sincerely,
The VASP team

[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.

Requirements: 

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.