News

Looking for Grad Students interested in Physics-Based Design of Therapeutics!

There are 1-3 openings for fall or winter 2020/2021 in Professor Ré Mansbach’s lab. Professor Mansbach is starting the lab in the Physics Department at Concordia University (http://www.concordia.ca/artsci/physics.html) in Montreal, Quebec, CA. There will be an associated tuition waiver and stipend for highly qualified/talented students.

Desired qualifications:  Looking for highly motivated graduate student candidates interested in theoretical and computational biophysics and deep learning.  Physics or Biophysics BA or BS is preferred but CS, biomedical engineering or related fields are welcome to apply. Experience with coding will be valuable, particularly in Python, and prior experience with molecular dynamics simulations will also be useful. 

Overview of Scientific Goals: Proteins are the building blocks of living things, miniature motors that make all of your cells function. Proteins embedded in cell membranes filter out toxic materials or uptake necessary nutrients. Meanwhile, malfunctioning proteins are responsible for a slew of disorders, including Alzheimer’s, type II diabetes, and Parkinson’s. Professor Mansbach want to understand and design small molecules and peptides for therapeutic applications such as finding new analgesics for treatment of chronic pain disorders, or correcting dysregulation of proteins that lead to Alzheimer’s. 

Potential Projects:

• Drug Design for Antibiotics. Exploring the use of a fragment-based approach for novel antibiotic hybrid design through generative deep learning, in which a library of fragments relevant to antibiotic applications will be used as a basis for a generative model with a particular emphasis on interpretability as well as candidate generation. 
• Antimicrobial Peptide (AMP) Design. Designing a deep learning model informed by multiscale molecular dynamics, wherein generative learning is used to iteratively create potential AMP candidates that are assessed on multiple scales using molecular dynamics.
• Theory of disulfide bonds for toxin-based therapeutics. Using polymer theory or molecular dynamics, exploring ways to bring together constrained polymer theories with bond-breaking force field models to map out the free energy landscapes of disulfide-rich toxins for treatment of pain.

Professor Mansbach is also happy to work with students on their own ideas—as long as they fall within the broad scope of my work—and/or to tailor projects to suit specific strengths and interests.

This lab would like to cultivate an inclusive, diverse and collaborative lab environment. Members of traditionally underrepresented groups in STEM are strongly encouraged to apply.

 

Please contact ramansbach@gmail.com to apply!

 

Note for students unfamiliar with the Canadian graduate school system: generally, you need a MSc to advance to the PhD, although fast-tracking is possible if we can make a strong case for it. Professor Mansbach is happy to take someone for a MSc and  commit to sponsoring them on through the PhD; Professor Mansbach also happy to take students who solely want a MSc.

Senior Scientist – Artificial Intelligence for R&D with BASF

Location: Wyandotte, MI (preferred) or Tarrytown, NY

BASF is seeking a professional like you to join the Data Science community and shape the future of digitalization in Research and Development.  An Artificial Intelligence System (AI) observes its environment and takes actions that maximize its chance of success at a given goal. As AI expert, you will create such systems that learn and decide for multiple application areas in materials and systems research as well as process and quality control.

BASF will look to you to have demonstrated that you can build AI systems based on machine learning technology by integrating advanced algorithms with interfaces for sensors and actors.  In contrast to an isolated developer, you appreciate and understand experts from all areas of Data Science, including Automation & Information Technology, Data Analytics, and Data Management.  This is an excellent opportunity for you to demonstrate your experience in multiple projects using different IT environments (e.g. Lab Automation systems like LabView, Big Data systems like Hadoop; Machine Learning frameworks like scikit-learn; and programming languages like R or Python), your thorough understanding of data driven workflows, and your knowledge of statistical methods and their industrial application.  You will also share your agility, creativity, and entrepreneurial thinking with the team while feeling comfortable in a diverse, interdisciplinary, and challenging environment.  Your superior consultancy mindset with a high degree of flexibility and an ability to learn and adapt to new applications quickly will also be key to your success in this role.

Qualifications

• BASF recognizes institutions of Higher Education which are accredited by the Council for Higher Education Accreditation or equivalent
•  Leveraging your PhD in science or engineering with a focus on quantitative analytics (or equivalent qualification) and your experience in the field of Machine Learning and Artificial Intelligence – so much so that you are recognized as an expert, you will evaluate new approaches and explore new applications for AI.
• Your experience in the Chemical (or related) industry and your excellent project management and collaboration skills will be essential as you initiate, execute, and lead AI projects in collaboration with research and business units in diverse high performing teams.
• Demonstrating your international and industrial working experience, including your proven track record in working within multinational teams, filling the innovation pipeline, and executing projects with high business impact, you will define and develop novel AI solutions and liaise with academic experts and commercial providers.
• Your excellent communication and interpersonal skills, along with your proven track record of relevant publications in peer-reviewed journals, will serve you well as you communicate and disseminate AI to a broad community of scientists and managers.

Visit the job positing for more information or to apply for the position.

Computational Solid Mechanics and Materials Modeling Postdoc position available at Los Alamos National Laboratory

The Fluid Dynamics and Solid Mechanics Group (T-3) is seeking outstanding candidates for multiple post-doctoral research opportunities in the areas of solid mechanics, materials modeling, and numerical methods development. The prospective post-doctoral research associates will take lead roles in the development of (i) models of inelastic deformation and/or failure in solid materials, and (ii) computational techniques for the effective representation of such behaviors.

Strong background and demonstrated research experience in theoretical modeling and numerical simulation of large-deformation problems involving metallic, polymeric, and/or organic crystal systems.  Strong interest and expertise in the mechanics and physics of plasticity and damage processes, and models for the representation of these behaviors.  Demonstrated experience with computational methods for the solution of initial/boundary value problems and/or implementation of new material models. Excellent oral and written communication skills, the ability to work independently and as an integral part of a team conducting theoretical/computational and experimental work, and the ability to interact effectively with other members of such a diverse team of specialists.

Desired Skills: Expertise in one or more of the following areas is highly desirable.

• Classical and/or single-crystal plasticity models,
• Poly-crystal plasticity with homogenization or full-field (FEM or spectral) methods,
• Microstructure evolution processes,
• Damage and failure models for ductile and/or brittle materials,
• Phase field models of microstructural evolution or damage/failure processes,
• Polymer strength/damage models,
• Nonlocal/generalized continuum mechanics and constitutive modeling,
• Coupled multi-physics problems,
• Concurrent multiscale modeling techniques,
• Data-driven / machine-learning approaches,
• Algorithm and code development for massively parallel and emerging architectures.

Education: A doctoral degree in engineering, materials science, scientific computing, or a related field, completed within the last five years or soon to be completed.

Additional Details: Position does not require a security clearance.  Selected candidates will be subject to drug testing and other pre-employment background checks.

 

To apply for this position or to learn more about it, please visit the Los Alamos National Laboratory job posting here.

Argonne Training Program on Extreme-Scale Computing (ATPESC)

Apply now for an opportunity to learn the tools and techniques needed to carry out scientific computing research on the world’s most powerful supercomputers. ATPESC participants will be granted access to DOE’s leadership-class systems at the ALCF, OLCF, and NERSC.

Who: Doctoral students, postdocs, and computational scientists
What: Intensive two-week training program
When: The annual ATPESC is set to take place July 26–August 7, 2020.

The call for applications is now open through March 2, 2020.
Apply now!

Eligibility

ATPESC provides advanced training to 70 participants.  Qualified applicants must have:

• Substantial experience in MPI and/or OpenMP programming
• Experience using at least one HPC system for a reasonably complex application
• Plans to conduct computational science and engineering (CSE) research on large-scale computers

Anyone who has attended ATPESC in a previous year is not eligible to apply.

1. Foreign nationals from countries that appear on a restricted foreign country listing in section 15 CFR 740.7 License Exceptions for Computers (namely, Cuba, Iran, North Korea, Sudan, and Syria) require a very lengthy approval process for access to DOE supercomputers, which will not complete in time for ATPESC 2020.  Therefore, citizens of these countries are not eligible to apply for participation in ATPESC 2020.  
2. Foreign nationals from countries other than those listed above will need a valid passport, visa, and supporting documents with an expiration date beyond the end of the training program (August 7, 2020).  Selected applicants will be provided a letter of invitation for visa purposes if needed.
3. Depending on the country of citizenship, the process for approval of accounts can take anywhere from weeks to months, and we cannot guarantee successful approval.  Selected applicants whose accounts have not been approved by July 2, 2020, will not be able to participate in ATPESC 2020. 

Applicants are expected to be proficient in English in order to take full advantage of this training program.  Results from TOEFL/IELTS/PTE examinations are not required.

Applicants must commit to attend the entire program.  If they fail to do so, they will be responsible for their travel, lodging and meal expenses.

Application Instructions

Applications must include (in this order):

1. Statement of Purpose (SOP)

This document should include your purpose for attending the training program, a description of your computing experience, and your current/future CSE research plans.  This part of the application should be no longer than two pages.  Please include your First and Last Name, Job Title, and Institution at the top of the document.

• Note: the curriculum of the training program is geared for participants with substantial MPI and/or OpenMP programming experience who have used an HPC system for a reasonably complex application and have conducted or are preparing to conduct CSE research on large-scale computers.  Please be prepared to describe in detail your previous experience with HPC systems (e.g., size of simulations, kind of systems, experience as user and/or developer, software and solvers used, etc.) and your future CSE plans on leadership-class supercomputers.

2. Curriculum Vitae (CV)

CVs must provide all science and technology specialties, all work positions with no gaps in time, and current/accurate names of all academic institutions attended.

3. Letter of recommendation (LOR)

One letter of recommendation from an advisor or supervisor.

• Note: if your advisor or supervisor wants to make a confidential recommendation, they should send it directly to support@extremecomputingtraining.anl.gov before the submission deadline.  In that case, ATPESC support staff will attach it to your application.  Please note that you are still responsible for ensuring final submission by your advisor or supervisor.  Incomplete applications will not be reviewed.

Please do not include in your application any articles, additional letters, or other documents that are not among the three mentioned above.

When you have compiled all the documents listed above:

1. Please create one PDF file with all items: SOP, CV, and LOR (if not confidential).
2. Log in to EasyChair to apply for ATPESC 2020

On the EasyChair application form:

Please use standard capitalization (i.e., “John Smith”; not “JOHN SMITH” nor “john smith”) and use your full legal name as it appears on your government-issued photo ID (e.g., passport).

• • For the Author Information, complete the Author 1 fields with your information and leave the other author fields blank. Please enter legal first and last name as it appears on your government-issued documents.
  • For the Title, use your legal first and last name followed by “Application” EXAMPLE: John Smith Application.
  • For the Abstract, enter a short paragraph summarizing your purpose for attending and the name of the advisor or supervisor that will be submitting your letter of recommendation (if not included in your submission PDF file).

• For the Keywords, enter your Country of Citizenship, CSE Field (from the list linked here) and Job Title (Ph.D. student, Postdoc, Scientist, Researcher, Engineer, etc).
• Upload your PDF file.
• Click on the submit button only once.

Other important dates:

• March 2, 2020 – Deadline to submit applications
• April 13, 2020 – Notification of acceptance
• April 24, 2020 – Registration deadline for accepted applicants
• May 1, 2020 – Registration deadline for wait-listed applicants

 

If you have any questions, please contact ATPESC staff.

For more information, please visit the website.