Temporary Research Assistant Positions at the U-M Center for the Assessment of Tobacco Regulations (CAsToR)

By | General Interest, SC2 jobs

Center for the Assessment of Tobacco Regulations LogoThe Center for the Assessment of Tobacco Regulations (CAsToR) aims to provide evidence-based and expert-informed modeling of the behavioral and public health impacts of tobacco regulations. Funded through the TCORS 2.0 program, this multi-institutional Center includes experts in the field of tobacco regulatory science and modeling from the University of Michigan, Georgetown University, Yale University, University California San Francisco, and University of Minnesota.

CAsToR is looking to hire up to two temporary research assistants in the University of Michigan School of Public Health Epidemiology Department, one who will primarily work on a nicotine reduction agent-based model and another who will support a pilot project using machine learning techniques to look at smoking status transitions. The ideal candidates are current graduate students (masters or doctoral level).

  • Temporary Part Time Position (Research Assistant II)
  • Hours: Up to 20 hours per week, remote preferred
  • Pay Rate: $15-28 dependent upon experience

Apply Now

Scholarships for tobacco simulation modeling course offered by the Center for the Assessment of Tobacco Regulations (CAsToR)

By | Educational, Funding Opportunities

The Center for the Assessment of Tobacco Regulations (CAsToR) is pleased to once again announce the availability of scholarships for a short course on tobacco simulation modeling (EPID 730) to be offered during the University of Michigan Summer Session in Epidemiology (SSE) Program in 2022. Scholarships are also available for Complex Systems Modeling for Public Health Research (EPID 793), in conjunction with EPID 730 scholarships.

EPID 730 will be offered as an on-line course and will provide an introductory overview of computational modeling techniques with examples in Tobacco Regulatory Science, discussions of best practices, and hands-on lab experience in which students will develop their own simulation models. At the completion of the course, students will be able to explain the contributions of simulation modeling in Tobacco Regulatory Science, and describe advantages and disadvantages of common modeling approaches. Students will explore how to incorporate simulation modeling into their own Tobacco Regulatory Science research and participate in interdisciplinary teams that use modeling techniques.

The deadline to apply is April 6th, 2022, and applicants are asked to wait to apply to the SSE program until after receiving notification of the scholarship funding decision in late April. Please see the flyer for more information and a link to the application.

Center for the Assessment of Tobacco Regulations Logo

New physics-based computation and AI framework to understand the agressive behavior of cancer cells

By | Feature, Research

Cancer is an illness caused by an uncontrolled division of transformed cells, which can originate in almost  any organ of the body.  Cancer is not a single disease, even when it arises in the same site of the body. Tremendous variability exists in progression of disease and response to therapy among different persons with the same general type of cancer, such as breast cancer. Even at the level of a single person, cancer cells show tremendous heterogeneity within a single tumor and among a primary tumor and metastases. This heterogeneity causes drug resistance and fatal disease. The prevailing dogma is that heterogeneity among cancer cells arises randomly, generating greedy individual cancer cells that compete for growth factors and optimal environments. The rare “winners” in this competition survive and metastasize. However, tumors consistently maintain heterogeneous subpopulations of cancer cells, some of which appear less able to grow and spread. This observation prompted Gary and Kathy Luker, cancer cell biologists at the University of Michigan, to hypothesize that cancer cells may actually collaborate under some circumstances to cause disease and not just compete. The idea that single, heterogeneous cancer cells work collectively within a constrained range of variability to drive population-level outputs in tumor progression is a ground-breaking concept that may revolutionize how we approach cancer biology and therapy.

The team is using innovative approaches to extract and merge data streams from models that generate heterogeneous cell behaviors

...cancer cell biologists have teamed up with computational scientists and experts in artificial intelligence to focus the power of these fields on understanding and overcoming heterogeneity in cancer.

To understand causes of single-cell heterogeneity in cancer and conditions that motivate cancer cells to collaborate, an interdisciplinary team of scientists at UM formulated an entirely new conceptual approach to this challenging problem. The cancer cell biologists have teamed up with computational scientists and experts in artificial intelligence to focus the power of these fields on understanding and overcoming heterogeneity in cancer. Building on large, single-cell data sets unique to the team, they will combine inverse reinforcement learning, an artificial intelligence method typically applied to discover motivations for human behaviors, with computational models inferred on the basis of the physics and chemistry of cell signaling and migration. They have proposed an entirely new conceptual approach combining single cell data, physics-based modeling and artificial intelligence to single-cell heterogeneity and intercellular interactions. By discovering  testable molecular processes underlying “decision-making” by single cells and their “motivations” for acting competitively or collaboratively, this research blazes a new path to understand and treat cancer. Their high-risk, high-reward approach to understand how each cell in a population processes information and translates that to action driving cancer progression, has attracted an award of $1 million dollars by the Keck Foundation. 

The team includes Gary Luker (Radiology, Microbiology and Immunology; Biomedical Engineering), and Kathryn Luker (Radiology), who are leading the experimental studies of cell signaling and migration; Jennifer Linderman (Chemical Engineering; Biomedical Engineering); and Krishna Garikipati (Mechanical Engineering; Mathematics), who are leading the machine learning and modeling side of the project. Nikola Banovic (Electrical Engineering and Computer Science) and Xun Huan (Mechanical Engineering) are using artificial intelligence approaches to discover decision-making policies and rewards for cancer cells, working with the rest of the investigators to incorporate experimental data and physics/chemistry-based models into their approaches.

The W. M. Keck Foundation was established in 1954 in Los Angeles by William Myron Keck, founder of The Superior Oil Company. One of the nation’s largest philanthropic organizations, the W. M. Keck Foundation supports outstanding science, engineering and medical research. The Foundation also supports undergraduate education and maintains a program within Southern California to support arts and culture, education, health and community service projects. This project incorporates elements from all the W. M. Keck Foundation’s focus research areas to tackle cancer with a novel, physics-based modeling and AI-centered approach.  The idea for this project originated in the 2020 MICDE faculty workshop in AI for Physically based Bio-medicine Workshop. The workshop brought together an interdisciplinary group of faculty members to discuss ways to advance artificial intelligence and machine learning methods for biomedical problems. After seeding the idea, a subset of these researchers were awarded an MICDE catalyst grant and a MIDAS PODS grant. These funds were used to establish the proof of concept and to generate preliminary results. 

Computational science is becoming increasingly indispensable in many areas of biomedical science. While the current proposal focuses on cancer, this innovative computational framework represents a transformative leap with widespread applications in multiple other biomedical, physical, and social sciences. MICDE supports innovative and interdisciplinary projects aiming to advance the current paradigms.

Portraits of Kathryn Luker, Gary Luker, Krishna Garikipati, Jennifer Linderman, Nikola Banovic and Xun Huan

Project’s principal investigators (left to right): Kathryn Luker (Radiology), Gary Luker (Radiology, Microbiology and Immonology, and Biomedical Engineering), Krishna Garikipati (Mechanical Engineering, and Mathematics), Jennifer Linderman (Chemical Engineering, and Mathematics), Nikola Banovic (Electrical Engineering and Computer Science) and Xun Huan (Mechanical Engineering).

An Academic and Research Career Workshop for Women in Computational and Data Sciences

By | Educational, Events

The Oden Institute for Computational Engineering and Sciences at UT Austin, Sandia National Laboratories (SNL), and Lawrence Livermore National Laboratory (LLNL) are partnering to host Rising Stars in Computational and Data Sciences, an intensive workshop for women graduate students and postdocs who are interested in pursuing academic and research careers.

The workshop will be held on April 20-21, 2022, in Albuquerque, NM.

The organizers are seeking nominations for outstanding candidates in their final year of PhD or within three years of having graduated. Approximately 30 women will be selected to come to Albuquerque for two days of research presentations, poster sessions, and interactive discussions about academic and research careers, with financial support for travel provided.

The nomination form requires (1) a letter of nomination and (2) a copy of the nominee’s resume.

Full details, including the nomination form and highlights from previous events can be found here.

Please consider nominating one of your outstanding current/recent PhD students or postdocs.

Nominations are due February 18, 2022.

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

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

By | SC2 jobs

PROJECT OVERVIEW
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.

REQUIRED QUALIFICATIONS
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

PROGRAM OVERVIEW
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.

PROGRAM STRUCTURE
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.

BENEFITS
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.

KEY DATES FOR 2021
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.”