Helmholtz Information and Data Science Academy Visiting Researcher Grant

By | Educational, SC2 jobs

Are you a doctoral researcher or Postdoc and your research has a strong link to the (applied) data and information sciences? The Helmholtz Visiting Researcher Grant offers doctoral students and Postdocs the opportunity to do a fully-funded short-term research stay (1 – 3 months) at one of the 18 Helmholtz centers. With more than 43,000 employees and an annual budget of 5 billion euros, Helmholtz is Germany’s largest scientific organization. Its research fields include: Energy; Earth and Environment; Health; Aeronautics, Space and Transport; Matter, and Information.

The Helmholtz Visiting Researcher Grant is promoted by HiDA, the Helmholtz Information and Data Science Academy. Its aim is to enable new research collaborations, to foster knowledge exchange, and to explore new or emerging research topics in the field of information and data sciences. The program addresses researchers in both academia and in industry. It offers researchers the opportunity to get to know the Helmholtz Association of German Research Centers.

Next Application Deadline: 15 March, 2022

For more information: https://www.helmholtz-hida.de/en/new-horizons/helmholtz-visiting-researcher-grant/

Info Session on the Program (via Zoom) on Tuesday, 18 January, 2022, 14.00 – 15.00pm  CET

Sign up here: https://tms.aloom.de/info-session-hida-research-grants-/

Postdoctoral position in neuroscience in the Renart Lab, Champalimaud Foundation, Portugal

By | Educational, SC2 jobs

The Renart Lab, in the Champalimaud Centre for the Unknown (Lisbon, Portugal), is looking for candidates for a postdoc position in within a project whose goal is to understand the neural basis of simple sensory judgements using modern methods in system’s neuroscience together with theory.

Successful applicants are expected to have experience studying controlled behavior in rodents using recordings and perturbations. The project has a strong quantitative component, so experience on computational neuroscience and statistics/machine-learning methods for behavioral and neural data analysis will be highly valued.
Some recent publications and methodologies relevant for the project are:
The Champalimaud Neuroscience Programme is a vibrant research community focussed on understanding the links between neural activity and behavior. The Renart lab promotes a horizontal and collaborative environment. The position offers a competitive salary and is available immediately for a duration of 3 years (with flexibility).

Interested applicants should send their CV, a brief motivation statement and the names of at least 2 references by email to:
careers@research.fchampalimaud.org and alfonso.renart@neuro.fchampalimaud.org

Flagship Pioneering Summer Fellowship Opportunity Information Session

By | Educational, SC2 jobs

Flagship Pioneering is a life science venture creation firm based out of Cambridge, MA. Flagship’s unique venture creation process is behind companies such as Moderna, Rubius Therapeutics, Indigo Agriculture, and several dozen others.

The Flagship Pioneering Summer Fellowship Program is a one-of-a-kind opportunity to work alongside scientist-entrepreneurs at the earliest stage of ideation and develop the next breakthrough life science companies. Over the course of an immersive 12-week paid program, you will be exposed to our proprietary innovation process, connect with scientific and business leaders within our vast ecosystem, and assess employment opportunities.

Ideal candidates are creative Ph.D., M.D., M.S., or science-oriented M.B.A. students that are within 1 year of graduating upon starting the fellowship or have recently graduated. Applications are rolling, but interested candidates are strongly encouraged to apply before January 31, 2022. 

During the 1-hour information session, you will learn about Flagship Pioneering from Associates Ayse Muñiz, PhD (University of Michigan Class of 2021), and Rahi Punjabi who will discuss a new AI Fellows track launching this summer. Those with a strong background in computer science, statistics, applied mathematics, physics, and computational biology are encouraged to attend to learn more about this new track.
DateThursday, January 13, 2022
Time: 11AM-12PM

Zoom info: https://flagshippioneering.zoom.us/j/96455167438?pwd=eWYxOURrZXkveUY3VHlMTnl6ZW9Gdz09&from=addon

Password: 756102

Applications to the 2022 Annual Argonne Training Program on Extreme-Scale Computing are due March 1

By | Educational, HPC

Argonne Training Program on Extreme-Scale ComputingThe annual Argonne Training Program on Extreme-Scale Computing (ATPESC), for doctoral students, postdocs, and computational scientists, is set to take place July 31-August 12, 2022. This year’s program will mark the 10th anniversary of ATPESC.

Submit your application for an opportunity to learn the tools and techniques needed to carry out computational science on the world’s most powerful supercomputers. ATPESC participants will have access to DOE’s leadership-class systems at the ALCF, OLCF, and NERSC.

Learn more and apply here

Renowned scientists and leading HPC experts will serve as lecturers and guide the hands-on sessions. The core curriculum will cover:

  • Hardware architectures
  • Programming models and languages
  • Approaches for performance portability
  • Numerical algorithms and mathematical software
  • Performance measurement and debugging tools
  • Data analysis, visualization, and methodologies for big data applications
  • Approaches to building community codes for HPC systems
  • Machine learning and data science

Doctoral students, postdocs, and computational scientists interested in attending ATPESC can review eligibility and application details on the website.

There are no fees to participate in ATPESC. Domestic airfare, meals, and lodging are also provided.
Application deadline: March 1, 2022.

ATPESC is funded by the Exascale Computing Project, a collaborative effort of the DOE Office of Science’s Advanced Scientific Computing Research Program and the National Nuclear Security Administration.

Idaho National Laboratory (INL) Graduate Fellowship Program

By | Educational, SC2 jobs

Idaho National Laboratory is now accepting applications for the INL Graduate Fellowship program. This program is designed to identify exceptional graduate students in research areas aligned with INL’s strategic agenda to enable the current and future mission of the lab. A collaboration between INL and universities, the INL Graduate Fellowship program provides mentoring and financial support for outstanding students who are enrolled, or plan to enroll, in graduate degree programs. Selected students will receive a salary of $60,000/year, plus tuition coverage from INL.

Flyer: INL_Graduate_Fellowship

How to apply

Applicants are invited to apply online through inl.gov/careers job posting numbers 16803 (for applicants in the fields of nuclear energy and clean energy development) and 16806 (for applicants in National & Homeland Security). Letters of recommendation should be submitted via email to graduatefellowships@inl.gov.

Important dates

  • February 13, 2022 – posting closes
  • May 2022 – selections will be announced

Postdoc Position: Computational Modeling in Immunology of Tuberculosis

By | SC2 jobs


About the Position:

An exciting opportunity is available for a strong mathematical/computational modeler to work in a multidisciplinary team on immune responses in the context of tuberculosis.  The position is available jointly in the laboratories of Jennifer Linderman in Chemical Engineering and Denise Kirschner in the Department of Microbiology and Immunology, both at the University of Michigan. The project uses a systems biology approach to integrate our multi-scale and multi-organ in silico models with data from humans and non-human primates derived by our collaborators. An estimated one-third of the human population is infected with the pathogen Mycobacterium tuberculosis, mostly in rural areas within developing countries, making it a critical global health issue.


  • Ph.D. degree (or equivalent) in engineering or mathematics or a closely related field
  • Strong computational skills and experience in mathematical modeling in biology
  • Desire and ability to read scientific literature in immune response to tuberculosis
  • Good communication skills and the ability to work in an interdisciplinary team are essential
  • Experience with python a plus
  • The ideal applicant will have extensive experience in object-oriented programming

How to apply:

Send a CV, names of 3 references, and a letter describing research interests and summarizing Ph.D. work to both Jennifer Linderman linderma@umich.edu and Denise Kirschner kirschne@umich.edu. Copies of papers authored by the applicant are welcome.  Those under-represented in STEM are especially encouraged to apply.

Postdoc Fellowship: Integration of biological system morphogenesis across scales and species through computational modeling

By | SC2 jobs

This project is related to Dr. Buganza Tepole’s effort as part of the Emergent Mechanisms in Biology of Robustness, Integration and Organization (EMBRIO) Institute. A core thrust of this Institute is to determine how multiple biochemical, biomechanical, and bioelectrical signals are integrated to control cell and organismal fate, how convergent and classical evolution have arrived at similar solutions to diverse biological problems, and especially how the integrative processes for morphogenesis scale from single cells to tissues to organisms.

As part of this Institute, Dr. Buganza Tepole leads the simulation and integration of mathematical models from different scales and species. To do so, physics-based models at different scales need to be rigorously up- and downscaled, expensive numerical solvers need to be replaced with efficient metamodels, and biological coupling terms needed for control of morphogenesis need to be identified from the data and simulations. The postdoctoral fellow sought in this project will help lead this core integration thrust. Advances in both traditional physics-based modeling and machine learning will be needed to carry out this integration. The Institute brings together a large group of PIs from different institutions, led by Dr. David Umulis, the Chair of the Weldon School of BME. More info.

Applicants with background on the following areas are sought:

  • Numerical solution of partial differential equations (PDEs)
  • Physics-informed machine learning

Additional qualifications that would make the application extremely competitive:

  • Experience in growth, remodeling and morphogenesis modeling

The goal of the Lillian Gilbreth Postdoctoral Fellowship Program at Purdue Engineering is to attract and prepare outstanding individuals with recently awarded PhDs for a career in engineering academia through interdisciplinary research, training, and professional development.

The Lillian Gilbreth Postdoctoral Fellows are selected not only for their outstanding scholarly achievements and proposed innovative interdisciplinary research but also for their potential for broader impact on industry and society. They undertake research with faculty mentors in different fields and participate in professional development activities tailored to their chosen path in academia.

Gilbreth Fellows will have two co-advisors. One faculty co-advisor must have a primary appointment in an Engineering school/division. The second must have a primary appointment in a different Engineering school/division or at a non-engineering department at Purdue. An additional third collaborator from within or outside Purdue can also participate in the project.

The Gilbreth Fellowship is a full time appointment and the Fellows undertake research with their faculty co-advisors, participate in professional development activities, and are required to prepare and submit short annual reports on their achievements.

Gilbreth Fellows are appointed for a two-year term, and receive an annual stipend of $70,000 and benefits. A $5,000 grant is also provided for professional development such as attending conferences or workshops and are mentored for their future academic careers through a variety of programs.

May 26, 2021: call to engineering faculty to post research topics on the LGPF website
July 15, 2021: website with proposed topics made live to interested applicants
October 31, 2021: Deadline to receive full application packets with recommendation letters
January 2022: the 2022 Lillian Gilbreth postdoc fellows announced; 2022 cohort fellows can start their assignments as early as February 2022.

Review the instructions for applying to the Lillian Gilbreth Postdoc Fellowship.

“Get non-Real”: Department of Energy grant funds novel research in High-Performance Algorithms at U-M

By | Feature, Research

“Preparing for the future means that we must continue to invest in the development of next-generation algorithms for scientific computing,

Barbara Helland, Associate Director for Advanced Scientific Computing Research, DOE Office of Science
Source: www.energy.gov/science/articles/department-energy-invests-28-million-novel-research-high-performance-algorithms

New research from the University of Michigan will help revolutionize the data processing pipeline with state-of-the-art algorithms to optimize the collection and processing of any kind of data. Algorithms available now are built for real data, meaning real numbers, however, most of the data we see on the internet is non-real, like discrete data, or categorical. This project is part of a $2.8 million grant from the Department of Energy on algorithms research, which is the backbone of predictive modeling and simulation. The research will enable DOE to set new frontiers in physics, chemistry, biology, and other domains. 

“Preparing for the future means that we must continue to invest in the development of next-generation algorithms for scientific computing,” said Barbara Helland, Associate Director for Advanced Scientific Computing Research, DOE Office of Science. “Foundational research in algorithms is essential for ensuring their efficiency and reliability in meeting the emerging scientific needs of the DOE and the United States.”

The U-M project, led by associate professor Laura Balzano and assistant professor Hessam Mahdavifar, both of electrical engineering and computer science, is one of six chosen by DOE to cover several topics at the leading-edge of algorithms research. According to the DOE, researchers will explore algorithms for analyzing data from biology, energy storage, and other applications. They will develop fast and efficient algorithms as building blocks for tackling increasingly large data analysis problems from scientific measurements, simulations, and experiments. Projects will also address challenges in solving large-scale computational fluid dynamics and related problems.

Laura Balzano and Hessam Mahdavifar portraits

Laura Balzano, associate professor of electrical engineering and computer science (left); Hessam Mahdavifar assistant professor of electrical engineering and computer science (right)

Balzano and Mahdavifar, both Michigan Institute for Computational Discovery and Engineering (MICDE) affiliated faculty members, will use a $300,000 portion of the overall grant to study randomized sketching and compression for high-dimensional non-real-valued data with low-dimensional structures.

“Randomized sketching and subsampling algorithms are revolutionizing the data processing pipeline by allowing significant compression of redundant information,” said Balzano. “Sketches work well because scientific data are generally highly redundant in nature, often following a perturbed low-dimensional structure. Hence, low-rank models and sketching that preserves those model structures are ubiquitous in many machine learning and signal processing applications.” 

Even though a lot of the data used and processed in scientific and technological applications are best modeled mathematically as discrete, categorical or ordinal data, most state-of-the art randomized sketching algorithms focus on real-valued data. To add to this, in practical applications, treating high-dimensional data can be challenging in terms of computational and memory demands. Thus, the proposed project will significantly expand the applicability of randomized sketching.

“A key to data-driven modeling is to carefully reformulate the computational and data analysis challenges and take full advantage of the underlying mathematical structure that is often common across application areas,” said Krishna Garikipati, MICDE director and professor of mechanical engineering and mathematics.”This research and the work that Laura and Hessam are doing is critically important to the advancement of computational discovery.”

Fall 2021 Information Sessions

By | Educational, Events

Fall 2021 information sessions on graduate programs in computational and data sciences at U-M

U-M graduate students interested in computational and data sciences are invited to learn about joint programs that will prepare them for success in computationally intensive fields. The programs are organized by the Michigan Institute for Computational Discovery & Engineering, and the Michigan Institute for Data Science. Both institutes offer vast training and networking opportunities, including webinar series, symposia and student centered events.

Two sessions are scheduled

The sessions will address:

  • The Graduate Certificate in Computational Discovery and Engineering: trains students in computationally intensive research so they can excel in interdisciplinary HPC-focused research and product development environments.

  • The Graduate Certificate in Data Science: focuses on developing core proficiencies in data analytics: modeling, technology and practice.

  • The Graduate Certificate in Computational Neuroscience: provides training in interdisciplinary computational neuroscience to students in experimental neuroscience programs, and to students in quantitative science programs, such as physics, biophysics, mathematics and engineering.

  • The  Ph.D. in Scientific Computing: open to all Ph.D. students who will make extensive use of large-scale computation, computational methods, or algorithms for advanced computer architectures in their studies. It is a joint degree program, with students earning a Ph.D. from their current departments, “… and Scientific Computing” — for example, “Ph.D. in Aerospace Engineering and Scientific Computing.”