University of Michigan’s Ph.D. in Scientific Computing: A history of supporting research through education

By | Educational, Feature

#Computationalscience everywhere!

Left side, 2167 configuration console for the IBM/System 360 Model 67-2 (duplex) at the University of Michigan, c. 1969 [Picture by Scott Gerstenberger – Scott Gerstenberger, Public Domain]

The University of Michigan’s joint Ph.D. program in Scientific Computing recently achieved a record enrollment of 137 students. Between 2015, when 15 students were enrolled -mainly from the Colleges of Engineering and Literature, Science and the Arts- and today, the program has witnessed an explosive growth of interest on the part of U-M students. The program now has students enrolled from over 30 departments spanning 8 different schools and colleges, and more than 130 students have graduated in the last 31 years, including 17 students to-date in 2020.

This popularity is emblematic of the dominant role that computation plays in the world today. With the breakneck pace at which new hardware and software architectures are being developed, the boom in simulation-based research in a growing number of disciplines, and the initiatives in data and computational sciences implemented at U-M in the last few years, including the establishment of the Michigan Institute for Computational Discovery & Engineering, and the Michigan Institute for Data Science (MIDAS), it may seem only natural that scientific computing should attract this level of interest. However, like all exceptionally successful undertakings, it owes a great deal to its past. We reached back more than three decades to piece together the history of the Ph.D. in Scientific Computing at U-M.

The broader history of computational science and high performance computing at the University of Michigan is rich and extensive. U-M has been at the forefront of Cyberinfrastructure research for many decades, marked by the acquisition of U-M’s first virtual memory computer in 1967, an IBM 360/67, one of the first computers of its kind in the world. This milestone was followed by many others, including further hardware acquisitions and establishment of new units to support advanced research computing. An important early step was taken in 1985 when the College of Engineering established the Laboratory for Scientific Computation (LaSC). LaSC’s goal was to foster and promote the use of scientific computation in research and instruction at U-M. During those years, several reports from national study committees established computational science as the third pillar of scientific methodology, along with theory and experimentation. Faculty members of LaSC, who were at the forefront of driving these trends  recognized that any initiative in this field needed to include a robust student training program. 

left: Prof. Kenneth Powell (Aerospace Engineering), director of the Ph.D. in Scientific Computing program since 2005; right: Prof. William Martin (Nuclear Eng. and Rad. Sciences), director of the program from 1989 to 2004.

Prominent at that time in LaSC were Prof. William “Bill” Martin (Nuclear Engineering and Radiological Sciences – NERS), the laboratory’s director, Prof. John Boyd (Atmospheric, Oceanic and Space Sciences), the laboratory’s associate director, and Prof. Edward Larsen (NERS), who was hired as part of the College of Engineering’s initiative to move aggressively in the area of scientific computing. Together, they designed a graduate academic program with the goal of giving students a more comprehensive training in numerical analysis and computer science than is typically possible within standard disciplinary programs housed within individual departments and schools. The fundamental idea was that, to excel in computational science and engineering, one must have a thorough understanding of the mathematical and physical problems to be solved, expertise in  the methodologies and algorithms, and a foundation in computer science to be able to apply this arsenal of techniques on modern computer platforms. The need for a thorough understanding of the physical problems led directly to the requirement that students had to be enrolled in a traditional Rackham degree program (i.e., a home department), while the need for mathematical underpinning and knowledge of algorithms and computer science topics led to the requirements for courses in numerical analysis, parallel algorithms, and related topics. The PhD in Scientific Computing program was approved by the State of Michigan in 1988, and enrolled its first students in 1989. This was well in advance of a wider recognition of the centrality of computation in academia and industry. It is true today, as it was in 1988, that students can apply to the PhD in Scientific Computing program from any Rackham-recognized PhD program at the UM. This unique and flexible administrative structure has enabled the rapid growth experienced in recent years as scientific computing has become an indispensable tool in many fields of academic endeavor. 

Prof. Quentin Stout, director of the Center for Parallel Computing 1992-2001 [Picture source: NASA Insights 1998]

The oversight of the degree program has evolved over the years as administrative structures around scientific computing have shifted. Regardless of its administrative home, the program has always been organized under the Rackham School of Graduate Studies. Originally, the College of Engineering had oversight of the program, with Prof. Martin appointed as director, and with guidance from the LaSC Education Committee. This setup continued through the merger of LaSC and the Center for Parallel Computing1 into the Center for Advanced Computing in 2001. In 2005, Prof. Kenneth Powell (Aerospace Engineering) was named director of the program succeeding Prof. Martin, and has continued in the role since. In 2008, the Office of Research Cyberinfrastructure (ORCI) was established, and the oversight of the program changed to the U-M Office of Research. In 2013, when ORCI was re-named as Advanced Research Computing, and the Michigan Institute for Computational Discovery & Engineering (MICDE) was born, oversight was transferred to MICDE.

Since its inception, the program has been described as intended for students who will make intensive use of large-scale computation, computational methods or algorithms in their doctoral studies. Although the requirements and goals of the program have not  changed in 31 years, the research applications, the algorithms and methodologies, and the computer platforms have been in constant evolution. Naturally, the courses offered in support of the program have followed closely. In 1989 the core research areas behind the program were computational fluid dynamics, advanced computer architectures, and particle transport, with the majority of the students coming from engineering, and mathematics. Still, students working in areas where computation was less recognized, such as AIDS transmission or social research projects, also were enrolled. Over the next two decades, the tremendous increase in simulation-based research by the faculty in engineering and physical sciences added many other focus areas, including material science, astronomy, and high energy physics, to name just a few. This growth added a new driver as data-intensive research gained importance in those fields. 

Prof. Suzanne Weekes, Associate Dean of Undergraduate Studies, ad interim, and Professor of Mathematical Sciences at Worcester Polytechnic Institute (U-M 1995, Mathematics and Scientific Computing) [Picture source: SIAM News Sept. 2020]

Several faculty members have had an important role shaping the program, by offering fundamental courses and providing mentorship. Notably, Prof. Quentin Stout, from Computer Science and Engineering, has had a prominent role in the program. He was the founding director of the Center for Parallel Computing, which  provided the basis for subsequent units in this sphere at U-M. He also developed, and has been teaching, Parallel Computing since 1985, innovating its curriculum to remain at the cutting edge of the current techniques, important aspects of which have been based on his own research. Other foundational courses, such as the Department of Mathematics’ Numerical Methods for Scientific Computing I & II and Numerical Linear Algebra have been offered for more than 30 years. More recently the Department of Physics course, Computational Physics, and the College of Engineering course, Methods and Practice of Scientific Computing, along with an array of courses in machine learning, have played prominent roles in transforming the curriculum in scientific computing as research in these areas has likewise redefined the field.

Unsurprisingly, the Ph.D. in Scientific Computing has produced many exceptional alumni. The first student graduated from the program in 1992, and notably for its time, two of the first four graduates were women, when gender imbalance was barely recognized. A majority of the program graduates went on to  positions in academia or the National Laboratories, with the rest working in varied fields in industry or government. Some of these outstanding alumni include Suzanne Weekes, U-M 1995 (Mathematics and Scientific Computing), currently Associate Dean of Undergraduate Studies, ad interim, and Professor of Mathematical Sciences at Worcester Polytechnic Institute. Prof. Weekes has recently been named SIAM executive director, and will start her new role on January 1, 2021.  Another alumna, Rona Oran, U-M 2014 (Space Science and Scientific Computing), is a computational plasma physicist at MIT and a member of the NASA team that is designing and planning a mission to the metal asteroid Psyche to be launched in 2020.

The current goal of the program is still founded on the original idea of strengthening the students’ foundations in methodology and computer science. The leadership of the program strives to bring computational science to more research fields, but importantly, aims to do so by enhancing diversity in the field. An important marker of U-M’s success on this front came in  2018 in the form of the Henry Luce Foundation’s award to the University of two Claire Boothe Luce Ph.D. fellowships for women to enroll in the Ph.D. in Scientific Computing. The program is committed to pursuing other such opportunities and creating an environment where students of all backgrounds and identities feel welcome and thrive.

1 In 1992 U-M was awarded a major equipment grant by the National Science Foundation to create a testbed of parallel computing architectures. The Center for Parallel Computing was established to operate the facility. The center installed and operated several different parallel computers over the years, including KSR-1, KSR-2, Convex Exemplar, SGI PowerChallenge, IBM SP2, and AMD and Apple clusters.

Los Alamos National Laboratory, Multiple HPC Intern Summer Opportunities

By | News, SC2 jobs

For questions about internships and instructions on how to apply, please email HPCRecruits@lanl.gov


HPC Data Movement and Storage Team: Upcoming Student Project Opportunities

PROJECT: EMERGING STORAGE SYSTEM(S) EVALUATION

(Lead Mentor: Dominic Manno)

Storage systems are evolving as technology, such as flash, becomes economically viable. Vendors implementing cutting edge hardware solutions often approach LANL to help gain insight into how these systems could move into the real world (HPC applications). Work in this area includes potential modifications to filesystems, filesystem configuration/tuning, testing hardware, fixing bugs, finding bottlenecks anywhere in the stack in order to increase efficiency and make the storage system faster.

Preferred skills:

● Interest in HPC and storage systems
● Comfortable with computer hardware
● Strong analytical skills
● Benchmarking experience
● Experience with linux and scripting (bash, csh, Python, etc.)
● Comfortable with C programming

PROJECT: FILE SYSTEM(S) FEATURE AND TOOLSET EVALUATIONS

(Lead Mentor: Dominic Manno)

File systems evolve along with user requirements. New features are implemented to accommodate changing workloads and technology. LANL’s storage team must evaluate new features and their impact on HPC applications. This work will explore file system features, modifications to current build procedures/processes, and impact to LANL’s storage team metric collection tooling. Work in this area includes building source code (kernel included), configuring linux servers, configuring a basic distributed file system, benchmarking, experiment design, analysis of data, and scripting.

Preferred skills:

● Knowledge of and interest in filesystems
● Experience with Linux and Command Line Interface
● Experience with code build systems and software
● Interest in HPC and storage systems at scale
● Benchmarking experience

ABOUT THE HPC DATA MOVEMENT AND STORAGE TEAM:
The High Performance Computing (HPC) Data Storage Team provides vanguard production support, research, and development for existing and future systems that feed and unleash the power of the supercomputer. The Data Storage Team designs, builds and maintains some of the largest, fastest and most complex data movement and storage systems in the world, including systems supporting 100 Petabytes of capacity. We provide storage systems spanning the full range of tiers from the most resilient archival systems to the pinnacle of high-speed storage, including all-flash file systems and systems supplying bandwidth that exceeds a terabyte per second to some of the largest and fastest supercomputers in the world. Innovators and builders at heart, the Data Storage team seeks highly motivated, productive, inquisitive, and multi-talented candidates who are equally comfortable working independently as well as part of a team. Team member duties include: designing, building, and maintaining world-class data movement and storage systems; evaluating and testing new technology and solutions; system administration of HPC storage infrastructure in support of compute clusters; diagnosing, solving, and implementing solutions for various system operational problems; tuning file systems to increase performance and reliability of services; process automation.


HPC Platforms Team: Upcoming Student Project Opportunities

PROJECT: HPC CLUSTER REGRESSION
(Lead Mentor: Alden Stradling)

Building on work done by our interns this summer, we are continuing the process of adapting existing regression testing software to do system-level regression testing. Using the LANL- developed Pavilion2 framework in combination with Node Health Check (NHC) for more detailed information, our interns are moving the system from proof-of-concept in a virtualized test cluster to production-style systems to measure effectiveness and system performance impact, and to flesh it out as a running system. Also on the agenda is to make test creation and propagation simple, allowing regression detection to be added at the same time as fixes are made to the system.

Preferred skills

• Interest in HPC and modern infrastructure management at scale
• Problem solving and creativity
• Configuration Management
• Version Control
• Programming experience in bash, python or perl
• Strong background in UNIX and familiarity using CLI

About the HPC Platforms Team
The High Performance Computing (HPC) Platforms Team provides vanguard system and runtime support for some of the largest and fastest supercomputers in the world, including multi-petaop systems (e.g., the recently deployed 40 Peta operations per second Trinity Supercomputer). Troubleshooters and problem- solvers at heart, the HPC Platforms Team seeks highly motivated, productive, inquisitive, and multi-talented candidates who are equally comfortable working independently as well as part of a team. Team member duties include: system deployment, configuration, and full system administration of LANL’s world-class compute clusters; evaluating and testing new technology and solutions; diagnosing, solving, and implementing solutions for various system operational problems; system administration of HPC network infrastructure in support of compute clusters; diagnosing, solving, and implementing solutions for various system operational problems; system software management and maintenance, including security posture maintenance; tuning operating systems to increase performance and reliability of services; developing tools to support automation, optimization and monitoring efforts; interacting with vendors; and communicating and collaborating with other groups, teams, projects and sites.


HPC Design Group: Upcoming Student Project Opportunities

PROJECT: OPTIMIZING “SPACK CONTANERIZE” FOR USE WITH CHARLIECLOUD

(Lead Mentor: Tim Randles)

The Spack software package manager has the ability to output software build recipes as dockerfiles. These dockerfiles often require hand-editing to work well with Charliecloud. In this project you will work with the Charliecloud team at Los Alamos to identify common problems with Spack dockerfiles. You will then determine if these problems are best addressed by making changes to Charliecloud’s dockerfile support or if there are improvements that should be proposed to Spack’s containerize functionality. The intern will be expected to implement suggested changes. At the end of the summer the intern will present their work.

PROJECT: BUILDING A GITLAB TEST INFRASTRUCTURE USING THE ANSIBLE REPOSITORY

(Lead Mentor: Cory Lueninghoener)

Use Gitlab’s CI/CD pipeline and runner functionality to build an automated test infrastructure for checkins to our Git-backed Ansible repository. This would start out with getting familiar with Gitlab’s automated pipeline capabilities and running tasks on code checkin, and move on to simple linting tests that run each time a change is checked in. From there, it could move on to running larger test suites on VMs or in containers, all the way up to building and testing virtual clusters and tagging good cluster image releases.

About the HPC DES Group:
The High Performance Computing Design Group focuses on future technologies and systems related to HPC while providing technical resources when needed to the more production focused HPC Groups. Areas of focus include I/O and storage, future HPC architectures, system management, hardware accelerators, and reliability and resiliency. Production timescales of projects vary from weeks in the future for production deployments to 10 years or more for some of the reliability and future architecture work.


Where You Will Work:
Our diverse workforce enjoys a collegial work environment focused on creative problem solving, where everyone’s opinions and ideas are valued. We are committed to work-life balance, as well as both personal and professional growth. We consider our creative and dedicated scientific professionals to be our greatest assets, and we take pride in cultivating their talents, supporting their efforts, and enabling their successes. We provide mentoring to help new staff build a solid technical and professional foundation, and to smoothly integrate into the culture of LANL.

Los Alamos, New Mexico enjoys excellent weather, clean air, and outstanding public schools. This is a safe, low-crime, family-oriented community with frequent concerts and events as well as quick travel to many top ski resorts, scenic hiking & biking trails, and mountain climbing. The short drive to work includes stunning views of rugged canyons and mesas as well as the Sangre de Cristo mountains. Many employees choose to live in the nearby state capital, Santa Fe, which is known for world-class restaurants, art galleries, and opera.

About LANL:
Located in northern New Mexico, Los Alamos National Laboratory (LANL) is a multidisciplinary research institution engaged in strategic science on behalf of national security. LANL enhances national security by ensuring the safety and reliability of the U.S. nuclear stockpile, developing technologies to reduce threats from weapons of mass destruction, and solving problems related to energy, environment, infrastructure, health, and global security concerns.

The High Performance Computing (HPC) Division provides production high performance computing systems services to the Laboratory. HPC Division serves all Laboratory programs requiring a world-class high-performance computing capability to enable solutions to complex problems of strategic national interest. Our work starts with the early phases of acquisition, development, and production readiness of HPC platforms, and continues through the maintenance and operation of these systems and the facilities in which they are housed. HPC Division also manages the network, parallel file systems, storage, and visualization infrastructure associated with the HPC platforms. The Division directly supports the Laboratory’s HPC user base and aids, at multiple levels, in the effective use of HPC resources to generate science. Additionally, we engage in research activities that we deem important to our mission.

Los Alamos National Laboratory, Supercomputer Institute Summer Internship Opportunity

By | News, SC2 jobs

PROGRAM OVERVIEW
The Supercomputer Institute is an intense, paid, 11-week, hands-on technical internship for people of all majors interested in the growing field of high-performance computing. You will obtain a thorough introduction to the techniques and practices of HPC; no HPS experience is required.

The program begins with two weeks of “boot camp”. Small teams of interns build, configure, test, and operate an HPC compute cluster starting from scratch, turning a head of equipment, cables, and electricity into a working mini-supercomputer that can run real HPC applications.

Next, the project phase begins. Teams of interns work under the guidance of HPC Division staff mentors on applied research and development projects that address real challenges currently faced by the division. Some projects use the mini-supercomputers built during boot camp, and others use existing LANL resources. These projects regularly influence the division as well as the field of high-performance computing.

Finally, teams present their accomplishments as a poster and technical talk to Laboratory management, staff, and fellow interns in an end-of-summer celebration of intern work.

The program runs June 1, 2021 – August 13, 2021.

View full job post here.

PROFESSIONAL DEVELOPMENT:
In addition to the technical portion of the program, interns also participate in fast-paced, focused professional development work, including:

  • Intense mentoring
  • Teamwork and professional collaboration
  • Resume writing and evaluation
  • Technical poster/presentation design and public speaking
  • Technical seminars on current HPC topics. Past seminars include high-speed networking, Linux containers,
  • parallel filesystems, facilities, and more.
  • Science lectures given by staff from across the Laboratory, from how the Mars Rover works to machine learning/ AI to black hole collisions.
  • Opportunities to sign up for tours of our world-class facilities, including the magnet lab, particle accelerator, million-core supercomputer, and ultra-cold quantum computer.

WHO IS ELIGIBLE TO APPLY:
The program is targeted to rising juniors or seniors, master’s students, and recent graduates with a bachelor’s or associate’s degree. Very highly qualified rising sophomores have been successful in the past, as well as occasional master’s graduates and Ph.D. students who can make a good case that they need hands-on practical training, rather than a research internship.

REQUIRED QUALIFICATIONS:
Interns must meet the following minimum requirements. If you are unsure whether you meet them, please ask us! We don’t want miss someone because they meet requirements in a way we did not anticipate.

  • Computer science, computer engineering, IT, or related experience/training.
  • Intermediate understanding of the Linux OS. For example, this might mean you have basic understanding of how an operating system works, some experience using Linux, and some knowledge of how Linux differs from desktop (e.g., Mac, Window) or phone OSes (Android, iOS).
  • Intermediate command line skills. You should have basic knowledge of the terminal using a shell such as tcsh or Bash. This doesn’t necessarily have to be on Linux (Macs also have a nice command line).
  • Scripting or programming experience of some kind.
  • Collegial, personable, plays well with others; the program is a team sport. Please note this does not mean you have to be “normal”; neurodiversity is encouraged.
  • Well-rounded and curious.
  • Can deal with reasonable deadlines. It’s a fast-paced program, but not high pressure.
  • Meets LANL undergraduate or graduate student program requirements, as applicable.

DESIRED QUALIFICATIONS:
In addition to the above, we’re looking for interns that also have some of the following skills. Note that few interns have all of them.

  • Strong communication skills (written and/or oral).
  • Interesting experience with Linux, hardware, networking, security, filesystems, etc.
  • HPC experience, whether sysadmin or user.
  • C or systems programming experience.
  • Interesting novel perspectives. Can you expand our horizons?

APPLICATION DEADLINE:
Deadline to apply is December 1, 2020.

HOW TO APPLY:
Apply via the instructions on this page. You’ll need to submit the following materials:

  • Current resume
  • Unofficial transcript, including GPA
  • Cover letter describing:
    • Your professional interests, experience, and goals
    • Why you are interested in the Supercomputer Institute
    • How you meet the minimum and desired skills above
    • What you hope to contribute to our team environment

ABOUT LOS ALAMOS:
Los Alamos is a small town in the mountains of northern New Mexico, located an elevation of 7,500 feet.

The town has an active intern community with various events such as free concerts. Outdoor activities are abundant, including hiking, camping, mountain biking, and rock climbing. Summers tend to be warm, and either dry or with afternoon monsoonal thunderstorms.

Stephen Timoshenko Distinguished Postdoctoral Fellowship at Stanford University

By | News, SC2 jobs

Stanford’s Mechanics and Computation Group (Department of Mechanical Engineering) is seeking applicants for a two-year term distinguished postdoctoral fellowship.

 

ABOUT THE FELLOWSHIP:

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. 

QUALIFICATIONS:

  • Research activities should be in the field of solid mechanics interpreted broadly. 
  • The candidate should be aligned with interests in the group, which include additive manufacturing, micro- and nano-mechanics, and bio-mechanics, with an interest in machine learning as it applies to the field of computational mechanics. 
  • Candidates will be given opportunities to develop their teaching experience by designing and teaching a class in the mechanics curriculum. 
  • This position is primarily targeting candidates who are seeking an academic career in a leading research university.
  • 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 more than 2 years before. 

APPLICATION DEADLINE: 

Fellowship applications are accepted year-round, with deadlines on October 1, December 1, April 1, and July 1. 

  • Applications received before these dates will be reviewed together. 
  • This position will close as soon as an offer is made and has been accepted by a candidate.

HOW TO APPLY:

Send your application by email to Kelly Chu, kchu22@stanford.edu

  • Email subject: Stephen Timoshenko Distinguished Postdoctoral Fellow search
  • All documents attached to the email should be PDF (Portable Document Format).

Application documents:

  • Cover letter (one page)
  • Curriculum vitae
  • List of publications
  • Brief statements of proposed research (up to three pages) and teaching (one page) 
  • Names and contact information of three recommendation letter writers

EEO STATEMENT:

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 welcomes applications from candidates who bring additional dimensions to the University’s research and teaching missions.

Some resources on diversity and inclusion at Stanford:

Reducing lung cancer mortality through modeling and simulations

By | Feature, Research

Lung cancer remains the leading cause of cancer related mortality in the US, and globally, accounting for 1.8 million deaths annually. Many of these deaths are preventable by the implementation of prevention strategies, including tobacco control policies and lung cancer screening recommendations, and by improvements in lung cancer treatment.  In the US, these policies have generally been implemented based on the analysis and outcomes of the population as a whole, although data analyses have shown that smoking and lung cancer rates, and access to healthcare and interventions, vary significantly by education, income, and race/ethnicity.

The Cancer Intervention and Surveillance Modeling Network (CISNET) Lung Working Group (LWG), led by Rafael Meza, associate professor of Epidemiology from the School of Public Health and MICDE member, has been awarded a new $8.5M grant to investigate the synergistic impacts of tobacco control policies, lung cancer screening and treatment interventions in the US and in middle-income nations. For the past 15 years, the CISNET LWG has contributed to the development of US national strategies for reducing the lung cancer burden by quantifying, through modeling and simulation, the impact of tobacco control on smoking, lung cancer, and overall mortality, as well as the population benefits and harms of lung cancer screening. This new grant will allow the group to expand their work to consider the impact of treatment improvements, including targeted therapies and immunotherapies,  and the synergies between treatment and prevention interventions. It also will enable the researchers to continue their work in addressing smoking and lung cancer disparities. The consortium uses a comparative modeling approach, where multiple, but distinct, models use the same data inputs, and aim to answer a common question with different approaches. This allows the group to assess the strengths and weaknesses of the different models, and aid the decision making process.

Established in 2000, CISNET is a consortium of NCI-sponsored investigators who use modeling and simulation to improve their understanding of cancer control interventions in prevention, screening, and treatment and their effects on population trends in incidence and mortality. CISNET is committed to bringing the most sophisticated evidence-based planning tools to population health and public policy. These models have been used to guide public health research and priorities, and have aided the development of optimal cancer control strategies. Besides lung cancer, CISNET also includes breast, colon, cervical esophageal and prostate cancer groups. 

We welcome 15 students to the 2020-21 class of MICDE graduate fellows

By | Educational, News

MICDE is proud to announce the recipients of the 2020 MICDE graduate fellowships. The fellows’ research projects involve the use and advancement of scientific computing techniques and practices. From political science, psychology, physics, and applied and interdisciplinary mathematics within the College of Literature, Science & the Arts to aerospace engineering, mechanical engineering, materials science engineering, industrial & operations engineering, and civil & environmental engineering within the College of Engineering, the 2020 MICDE fellows epitomize the reach of computation in diverse scientific disciplines.

For the past six years, MICDE has awarded fellowships to over 120 graduate students from our large community of computational scientists. The MICDE graduate student top-off fellowship provides students with a stipend to use for supplies, technology, and other materials that will further their education and research. Among other things, awards have helped many to travel to conferences and meetings around the world to share the rich and diverse research in computational science being carried out at U-M.

The awardees are:

Eytan Adler, Aerospace Engineering
Hessa Al-Thani,
Industrial and Operations Engineering
Zijie Chen,
Mechanical Engineering
Alexander Coppeans
, Aerospace Engineering
Xinyang Dong, Physics
Karthik Ganesan,
Psychology
Iman Javaheri, Aerospace Engineering
Huiwen Jia, Industrial and Operations Engineering
Daeho Kim, Civil and Environmental Engineering
Yudan Liu,
Chemistry
Emily Oliphant
, Materials Science and Engineering
Ryan Sandberg, Applied and Interdisciplinary Mathematics
Patrick Wu, Political Science
Zhucong Xi, Materials Science and Engineering
Yi Zhu, Civil and Environmental Engineering

Learn more about the fellows and the MICDE Fellowship program

Graduate Research Assistantships for Fall 2020 Term in Computational Multiphase/Multi-Physics Projects

By | News, SC2 jobs

Professor Jesse Capecelatro’s Computational Multiphase/Multi-Physics Flow Lab is seeking Three Graduate Students

 

Professor Jesse Capecelatro is a faculty member within the College of Engineering’s Mechanical and Aerospace Engineering departments. Prof. Capecelatro’s lab group is seeking current or recently graduated Master’s or Ph.D. students for paid Research Assistant positions starting in the Fall 2020 term. Read more about Prof. Capecelatro’s research group here.

Research Assistants will be working on one of three projects.

PROJECT #1: MODELING TURBULENT FLOWS WITH FINITE SIZE PARTICLES ON HETEROGENEOUS ARCHITECTURES

Description: The objective of this project is to develop a highly scalable direct numerical simulation (DNS) code that leverages new algorithmic advances in (a) turbulence simulation using a pseudo-spectral approach on heterogeneous architectures and (b) efficient scaling of particle dynamics with number of particles, to perform massive-scale simulations with a mixture of CPUs and GPUs. The student will work with Prof. Capecelatro at UM and collaborators at Iowa State and Georgia Tech. The majority of the code will be written in Fortran 90 and C.

This position is expected to last 1 year in duration with the possibility of extension, and work will be performed remotely. Compensation for this position will be based on experience and qualifications.

Desired Qualifications:

  • Major in Mechanical Engineering, Computer Science, or similar
  • Strong background in fluid mechanics
  • Good knowledge in turbulence
  • Excellent programming skills in a high-performance language like C, Fortran, Python
  • Familiar with parallel computing

PROJECT #2: MULTI-STEP EFFECTIVENESS FACTORS FOR NON-SPHERICAL CATALYSTS

Description: Prof. Capecelatro and his postdoc Aaron Lattanzi will provide support to the graduate student on development of new models for diffusion limited reaction schemes that will be delivered to the National Renewable Energy Laboratory (NREL). The multi-step effectiveness vector (MEV) previously derived by CO-PI Lattanzi will be expanded to account for cylindrical and infinite slab catalyst geometries. Reactant concentration profiles and volume-averaged reaction rates predicted by the new MEV will be directly compared to high-fidelity simulations conducted by NREL to verify the model.

This position is expected to last 9 months in duration with the possibility of extension, and work will be performed remotely. Compensation for this position will be based on experience and qualifications.

Desired Qualifications:

  • Major in Chemical Engineering, Mechanical Engineering, or similar
  • Excellent programming skills in a high-performance language like C, Fortran, Python
  • Strong background in fluid mechanics
  • Familiarity with chemical kinetics (CHE 344. Reaction Engineering and Design or similar class)

PROJECT #3: SENSITIVITY AND UNCERTAINTY QUANTIFICATION OF MODELING PARAMETERS FOR SIMULATING HIGH-SPEED MULTIPHASE FLOWS

Description: The student will perform a literature review on the state-of-the-art in modeling compressible particle-laden flows. Simulations will be performed of shock waves interacting with solid particles using our in-house high-speed multiphase flow solver (Fortran 90). A sensitivity analysis will be performed to quantify the effect of particle statistics on modeling parameters.

This position is expected to last 9 months in duration with the possibility of extension, and work will be performed remotely. Compensation for this position will be based on experience and qualifications.

Desired Qualifications:

  • Major in Aerospace Engineering, Mechanical Engineering, or similar
  • Excellent programming skills in a high-performance language like C, Fortran, Python
  • Familiar with uncertainty quantification, tools for sensitivity analyses
  • Strong background in fluid mechanics
  • United States citizenship

APPLY  TODAY!
Please send your CV, transcript, and a brief statement about your interests and background relative to the projects listed above to Professor Jesse Capecelatro jcaps@umich.edu with subject, “Fall 2020 Research Assistantship”.

Graduate Research Assistantships for Fall 2020 Term in Physics-based Data-driven Modeling Projects

By | News, SC2 jobs

Professor Julie Young’s Lab Seeking Two Engineering-focused Grad Students to Assist in Modeling Research

Professor Julie Young is a faculty member within the College of Engineering’s Naval Architecture and Marine Engineering, Mechanical Engineering, and Aerospace Engineering departments. Professor Young’s lab group is seeking graduate students (current or recently graduated master’s or Ph.D.’s) for paid Research Assistant positions starting in the Fall 2020 term. The expected time commitment for these positions is 20 hours per week.

Students will be working on one of two projects:

Project #1 Description: Development of a physics-based data-driven model for system identification and control of lifting surfaces in multiphase flow.

Project #1 Desired Qualifications:

  • Excellent programming skills
  • Good knowledge of system identification
  • Familiarity with data-driven models, control methods
  • Familiarity with experimental modeling and data analysis
  • Good knowledge of nonlinear fluid and structural dynamics
  • Engineering major or extensive coursework in engineering-related field
  • United States citizenship

Project #2 Description: Development of physics-based data-driven model for marine ship-propulsion system.

Project #2 Desired Qualifications:

  • Excellent programming skills
  • Good knowledge of system identification and data-driven models
  • Familiarity with experimental modeling and data analysis
  • Good knowledge of propulsion systems
  • Engineering major or extensive coursework in engineering-related field
  • United States citizenship

Compensation:
Compensation for these positions will be commensurate with experience and qualifications.

Apply Today!
Please send your CV, transcript, and a brief statement about your interests and background relative to the projects listed above to Professor Julie Young at ylyoung@umich.edu with subject, “Fall 2020 Research Assistantship”.

Alternatives Research & Development Foundation to Support Research on COVID-19, Aiming for Advancement in Non-animal Methods of Drug Discovery

By | News, Research

Pharmaceutical companies across the globe are racing to introduce clinically tested and approved therapeutic drugs that fight COVID-19 virus to market. As is typical in drug discovery research, animals have played a critical role in the development and testing of COVID-19 therapeutics. A proposal by U-M Professor Rudy J. Richardson, Dow Professor Emeritus of Toxicology, Professor Emeritus of Environmental Health Sciences, and Associate Professor Emeritus of Neurology at the University of Michigan, titled “Discovering host factor inhibitors in silico for SARS-CoV-2 entry and replication” has been awarded funding to identify compounds that bind to human proteins that facilitate entry and/or replication of the SARS-CoV-2 virus. Awarded, in part, because of its potential to develop alternative methods to advance science and replace or reduce animal use, this research will employ in silico ligand protein docking to discover existing drugs (repurposing) and/or new drug candidates capable of inhibiting host proteins involved in infection pathways for the COVID-19 virus, SARS-CoV-2.

Protein docking targets include four serine hydrolases. Using these targets, researchers will reversibly dock approximately 40,000 ligands from the Binding Database comprising FDA-approved drugs along with serine protease and PLA2 inhibitors, including organoboron compounds. Then, covalent docking will be conducted on a ligand subset containing pharmacophores capable of covalently binding serine hydrolases. Consensus ranking from four docking programs will be used to generate a penultimate list of candidate compounds. Those showing high predicted potency against off-target serine hydrolases will be excluded. The final list of compounds will be made publicly available for further evaluation in bioassays.

Professor Richardson’s grant, awarded by the Alternatives Research & Development Foundation, is a part of the ARDF’s 2020 Open Grants program, funding research projects that develop alternate methods to advance science and replace or reduce animal use. Although the immediate goal of this computational study supports the identification or development of a COVID-19 vaccine, the long-range vision is to advance computational and in vitro approaches to eliminate animal use from drug discovery for humans and other species. 

MICDE Affiliated Faculty member Rudy J. Richardson is a Dow Professor Emeritus of Toxicology and Professor Emeritus of Environmental Health Sciences within the School of Public Health, and Associate Professor Emeritus of Neurology within the Medical School at the University of Michigan.

Computational Biologist Postdoc-level Opening in Microbial Genomics

By | News, SC2 jobs

Air Force Research Labs Seeking Postdoc-level Computational Biologist for Microbial Genomics Position

 The Air Force Research Laboratory near Dayton, OH is seeking a Computational Biologist with experience in microbial genomics. The successful candidate will have the opportunity to work across multiple collaborative projects in the areas of microbial biofouling and corrosion, polymicrobial interactions of DoD relevant microbial communities, and mechanisms of polymer biodegradation. The selected candidate will use informatics to assemble, annotate, and perform comparative analyses of both bacteria and fungi to aid in generation of testable hypotheses. This role requires effective teamwork and communication of results with colleagues from diverse backgrounds.

Functions for this Role will also Include:

  • Transcriptomics of bacteria and fungi communities including differential expression analyses and identifying upregulated proteins
  • Analysis and interpretation of microbial community data using network analyses and visualization tools
  • Informatics on enriched and complex microbial communities generated from metagenomics and metatranscriptomics including assembly, binning, annotation, enzyme mining, and phylogenetic analyses
  • Documentation and presentation of research progress in the forms of reports, publications, and oral presentations
  • Travel to scientific reviews and conferences

Required Qualifications:

  • Ph.D. (or M.Sc. with 5+ years of relevant experience) in Computational Biology, Bioinformatics, or Microbial Genomics
  • Demonstrated success in applying open source software and in developing novel algorithms for the comparative analysis of genomes
  • Deep understanding of phylogenetic analyses and comparative genomics methods
  • Fluent in major scripting languages such as Python or Perl, and proficient in using the command line in Unix environments
  • Previous experience in comparative genomics or an understanding of secondary metabolite biosynthesis is highly desirable
  • Experience in applying machine learning algorithms or statistical methods to solving biological problems is desirable
  • Demonstrated understanding of relevant public genomic databases
  • Ability to work effectively in a multidisciplinary team, and communicate results clearly to non-computational scientists
  • Excellent organizational skills
  • This position is working on-site at a government facility and requires U.S. citizenship

Apply Today!

Apply via this link: https://ues.hrmdirect.com/employment/job-opening.php?req=1328012&&cust_sort1=-1&&nohd#job

If you have any questions, please email eric.harper.4@us.af.mil and vanessa.varaljay.1@us.af.mil.