Announcements

Seeking Applicants for Tenure-Track/Tenured Faculty Positions Across All Areas of Civil and/or Systems Engineering

The Johns Hopkins University’s Department of Civil Engineering (currently transitioning to the Department of Civil and Systems Engineering) seeks applicants for tenure-track/tenured faculty positions at all levels and across all areas of Civil and/or Systems Engineering. While there is an emphasis on research that impacts any of our growing research thrusts in resilient cities, safety and security, space exploration and habitation, future energy infrastructure and/or decision-making for health, all qualified applicants in any area of Civil and Systems Engineering will be considered.

The Department currently has 12 faculty whose research broadly covers the areas of Structures, Systems, and Mechanics of Materials. Current enrollments in the Department are approximately 21 undergraduate students, 21 masters students, and 51 doctoral graduate students. The department maintains laboratories and major facilities for research. Strong links to JHU institutes and centers such as the Malone Center for Engineering in Healthcare, the Center for Systems Science and Engineering, the Center for Integrated Structures-Materials Modeling and Simulation, the Hopkins Extreme Materials Institute, the Johns Hopkins Center for Additive Manufacturing and Architected Materials, and the Cold-formed Steel Research Consortium expand the footprint of the Department both within and outside of the University. More information about the Department can be found at http://www.ce.jhu.edu.

The Whiting School of Engineering comprises over 200 full time tenure-track, research, and teaching-track faculty in nine academic programs with a total annual research budget of over $170 million. Research partnerships with the Johns Hopkins School of Medicine, Applied Physics Laboratory, Bloomberg School of Public Health and the Krieger School of Arts and Sciences make the Whiting School of Engineering a unique research and educational environment. Student enrollment exceeds 1800 at the undergraduate level with over 1000 full time MS and PhD students. The Engineering for Professionals program enrolls over 4000 part time continuing education students and is the largest program of its kind in the country.

Applicants must hold an earned doctorate in an appropriate field by the time their appointment begins. Candidates must have a demonstrated record of outstanding independent research and excellence in teaching, professional service and translation. Applications at all levels will be considered; salary and rank will be commensurate with qualifications and experience. Applicants should submit a curriculum vitae, a research statement, a teaching statement, and three recent publications.  Applications must be made on-line at https://apply.interfolio.com/70230.

Candidates applying for Associate or full Professor positions should not provide any information for references. Candidates applying for the position of Assistant Professor should provide names and contact information of at least three (3) references. Review of applications will begin immediately.  While candidates who complete their applications by December 1, 2019 will receive full consideration, the Department will consider exceptional applicants at any time.

The Johns Hopkins University is committed to active recruitment of a diverse faculty and student body. The University is an Affirmative Action/Equal Opportunity Employer of women, minorities, protected veterans and individuals with disabilities and encourages applications from these and other protected group members. Consistent with the University’s goals of achieving excellence in all areas, we will assess the comprehensive qualifications of each applicant.

The Whiting School of Engineering and the Department of Civil Engineering are committed to building a diverse educational environment.

Post-Doctoral Fellowship: Network Modeling of Infectious Diseases

Post-Doctoral Fellowship: Network Modeling of Infectious Diseases

Applications are invited for a full-time postdoctoral position at the Department of Civil Engineering and the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University under the supervision of Associate Professor Lauren Gardner. The applicant will be expected to undertake and develop research on the topic of spatial epidemiology, with a focus on the development of models to infer outbreak risk factors, predict outbreaks, and optimize resource allocation for outbreak mitigation. Candidates should have expertise in one or more of the following areas: network modelling, optimization, machine learning, statistical modelling, and data visualization, with previous experience working on epidemiological applications.

The postdoc will work closely with an international multidisciplinary team of faculty and Ph.D. students across Engineering and Public Health. In addition, the PI will make every effort to mentor the postdoc for transition into a faculty position. This includes guidance on grant-writing, teaching opportunities, and translation of research. Women and Underrepresented Minorities are highly encouraged to apply. This is a year-long postdoc that can potentially be extended up to two years upon satisfactory performance and availability of funding.

SELECTION CRITERIA

The candidate will be expected to:

  • Possess a PhD degree in computer science, engineering, applied or computational mathematics, or a closely related field.
  • Have expertise in one or more of the following areas: network modelling, machine learning, statistical modelling, data visualization.
  • Previous experience working on epidemiological applications.
  • Strong programming and data visualization skills.
  • Demonstrated experience in analyzing large scale data sets.
  • Demonstrated experience in working on large-scale multi-disciplinary projects
  • The ability to work effectively as part of a multi-disciplinary research team
  • Illustrate the motivation and discipline to carry out autonomous research.
  • High level interpersonal, written and oral communication skills in English.
  • A record of research accomplishment as reflected in publications in peer-reviewed journals and conferences and presentations at scientific meetings.

Start date is flexible. Review of applications will begin immediately and continue until the position is filled. Complete applications should include the following (in a single pdf file) to l.gardner@jhu.edu:

(1) A cover letter

(2) A full curriculum vitae

(3) Up to two research publications and/or preprints

(4) The names and contact information for three references

(5) (Optional) A one-page original research proposal on the topic of your choosing, with the following headings: Motivation, Research Questions, Research Approach, Methods, Data Sources, Timeline.

Thesis Defense: Mikhail Osanov, “Topology Optimization for Additive Manufacturing: from Mechanical Components to Orthopaedic Implants”

THE DEPARTMENT OF CIVIL ENGINEERING

AND

ADVISOR JAMES GUEST, ASSOCIATE PROFESSOR

ANNOUNCE THE THESIS DEFENSE OF

Doctoral Candidate

Mikhail Osanov

Thursday, September 26, 2019

1:30PM

Shriver Board Room

“Topology Optimization for Additive Manufacturing: from Mechanical Components to Orthopaedic Implants”

Abstract:

Additive Manufacturing (AM) is a free-form fabrication technique that creates structures in a layer-by-layer fashion. Topology Optimization is a free-form, systematic approach to designing structures. These technologies are therefore well-suited for each other, but must be integrated to fully leverage their capabilities. This dissertation seeks to more tightly couple Topology Optimization by proposing several novel algorithms that improve manufacturability of the optimized parts and components. These include cylindrical projection method, which mimics the layer-by-layer nature of Additive Manufacturing processes, and several extensions to overhang projection methods for eliminating support structures, including providing access points for easy removal of support structures, eliminating internal voids, accounting for build plate post-processing costs, and utilizing the overhang constraint within the design of support structures. These algorithms are demonstrated on several design examples and solutions are shown to be directly manufacturable, thereby requiring less post-processing operations that can be time and cost intensive.

The final Chapter of this dissertation is dedicated to the design of femoral implants used in Total Hip Arthroplasty (THA). With the growing number of yearly total joint replacements and the demand for improved mobility and quality of life, the need for high-performance implants is apparent. In this work we seek to alleviate the existing clinical issue of stress shielding, pertinent to current state-of-the-art implants, through new designs using Topology Optimization. We propose a novel Topology Optimization formulation that is capable of addressing regional stress levels in bone by manipulating the topology of the implant, and demonstrate solutions that are predicted to reduce stress shielding effects compared to implants that are currently used in practice.

Thesis Defense: Anindya Bhaduri, “Adaptive Construction of Surrogate Functions for Various Computational Mechanics Models”

THE DEPARTMENT OF CIVIL ENGINEERING

AND

ADVISOR LORI GRAHAM-BRADY, PROFESSOR AND CHAIR

ANNOUNCE THE THESIS DEFENSE OF

Doctoral Candidate

Anindya Bhaduri

Friday, September 20, 2019

2:00PM

Latrobe 106

“Adaptive Construction of Surrogate Functions for Various Computational Mechanics Models”

Abstract:

In most science and engineering fields, numerical simulation models are often used to replicate physical systems. An attempt to imitate the true behavior of complex systems results in computationally expensive simulation models. The models are more often than not associated with a number of parameters that may be uncertain or variable. Propagation of variability from the input parameters in a simulation model to the output quantities is important for better understanding the system behavior. Variability propagation of complex systems requires repeated runs of costly simulation models with different inputs, which can be prohibitively expensive. Thus for efficient propagation, the total number of model evaluations needs to be as few as possible. An efficient way to account for the variations in the output of interest with respect to these parameters in such situations is to develop black-box surrogates. It involves replacing the expensive high-fidelity simulation model by a much cheaper model (surrogate) using a limited number of the high-fidelity simulations on a set of points called the design of experiments (DoE).

In this talk, a couple of examples related to newly developed adaptive black-box surrogate constructions are presented first to demonstrate the usefulness of surrogate construction in general. Then we consider an LS-DYNA simulation model of a continuum level plain weave S-2 glass/SC-15 epoxy composite plate under ballistic impact by a cylinder projectile. The goal is to develop an efficient computational framework for generation of probabilistic penetration response of the plate using surrogates. More specifically, this study involves construction of adaptive classification surrogates in order to generate two important quantities of interest, the probabilistic velocity response (PVR) curve as a function of the projectile impact velocity, and the ballistic limit velocity prediction as a function of the strength parameters of the plate model.

Announcing the Fall 2019 Graduate Seminar Speakers

Below is a listing for the speakers being showcased during the Spring 2019 Civil Engineering Graduate Seminar Series. All civil engineering graduate seminars are FREE and open to the public. Attendance is required for all enrolled Civil Engineering graduate students. For information on individual seminars, please refer to the Events Calendar.

For directions and information on parking please see Maps & Directions link at www.jhu.edu and select information on Homewood Campus.

 

Thesis Defense: May Thu Nwe Nwe, “Topology Optimization of Truss Structures Considering Stress and Stability Constraints”

THE DEPARTMENT OF CIVIL ENGINEERING

AND

ADVISOR JAMIE GUEST, ASSOCIATE PROFESSOR

ANNOUNCE THE THESIS DEFENSE OF

Doctoral Candidate

May Thu Nwe Nwe 

Thursday, August 1, 2019

8:30AM

Latrobe 106

“Topology Optimization of Truss Structures Considering Stress and Stability Constraints”

Abstract:

Topology optimization is a free-form approach to designing efficient structural layouts. Although highlighted repeatedly in literature for its ability to identify creative, high performance designs, it is also well known that oversimplification of the underlying optimization formulation can lead to impractical structural solutions. A common truss optimization problem formulation is to minimize linear elastic strain energy for a given structural mass (or minimize mass subject to a linear elastic deformation constraint). The optimal solutions obtained from such a formulation are independent of the direction of the applied load and often include members that may readily fail due to yielding or buckling, and/or colinear members with unbraced hinges that are unstable. Such solutions are, of course, impractical from structural engineering perspective. Incorporating stress and stability metrics into the topology optimization formulation, however, involves significant mathematical challenges primarily due to the possibility of vanishing members, which ultimately leads to singularities and disjointed regions in the design space that gradient-based optimization struggles to navigate. This thesis reviews and discusses these challenges and explores a disaggregated formulation where design and state variables are treated as free optimization variables to enable consideration of stress, (member) buckling, and global (system) stability metrics in a mathematically consistent manner. The impact of various constraints and objectives on the optimized solutions is demonstrated for simple truss design problems. The presented results clearly show that the incorporation of stress, member buckling, and global stability metrics results in more realistic design solutions. Future directions of research are also discussed.

 

 

Thesis Defense: Deniz Ozturk, “Multi-scale Modeling and Uncertainty Quantification of Deformation and Fatigue Crack Nucleation in Titanium Alloys using Parametrically Homogenized Constitutive Models”

THE DEPARTMENT OF CIVIL ENGINEERING

AND

ADVISOR SOMNATH GHOSH, PROFESSOR

ANNOUNCE THE THESIS DEFENSE OF

Doctoral Candidate

Deniz Ozturk

Friday, July 19, 2019

12:00pm

Latrobe 106

Multi-scale Modeling and Uncertainty Quantification of Deformation and Fatigue Crack Nucleation in Titanium Alloys using Parametrically Homogenized Constitutive Models

Abstract:

This thesis develops novel micromechanical and multi-scale models of deformation and fatigue crack nucleation in Titanium alloys. Image-based rate-, size- and temperature-dependent crystal plasticity finite element (CPFE) models are developed and calibrated from deformation experiments performed on macroscopic and single crystal specimens. Micromechanical analyses are performed on 3D polycrystalline statistically equivalent RVEs (SERVEs) to study the effects of microstructure, crystallography and thermo-mechanical loading conditions on fatigue crack nucleation in Titanium alloys. A probabilistic crack nucleation model is developed and calibrated from in-situ X-ray computed tomography observations of initiating cracks. The crack nucleation model, accounting for both time-dependent and cyclic damage mechanisms successfully reproduce the important characteristics of the experimental crack nucleation lives under both dwell and continuous cyclic loading conditions. Thermo-mechanical simulations of polycrystalline models suggest that an alleviation of the dwell effect at elevated temperatures results from the reduction of the critical resolved shear stress of <c+a> systems. The simulated orientations of initiated cracks are inclined 5°–25° off the crystallographic (0001) planes, in agreement with experimental measurements with micro-tilt fractography.

To predict fatigue crack nucleation in structural components of Titanium alloys, a two-way multi-scale modeling framework is developed next. A parametrically homogenized constitutive model (PHCM) and a parametrically homogenized crack nucleation model (PHCNM) are developed from computational homogenization of CPFE simulation results performed on microstructural SERVEs. A machine learning method is used to derive the microstructure-dependent constitutive parameters of PHCM and PHCNM based on micromechanical analysis data. Macroscopic FE models of test specimens are constructed based on EBSD scans of the material, accounting for microstructural heterogeneity and crystallographic microtexture. Macroscopic simulations of dwell and cyclic loading are performed and nucleation hotspots are identified by PHCNM. Top-down simulations of the local M-SERVEs are performed to probe microstructural fatigue crack nucleation sites and cycles. The computed distributions of nucleation lives and locations follow the experimentally observed characteristics of the dwell effect in Titanium alloys.

Finally, PHCMs are augmented with uncertainty quantification to account for model reduction errors, calibration data sparsity, and microstructural uncertainty. Microstructure-dependent functional forms of stochastic PHCMs are identified by machine learning and Bayesian inference techniques. A novel uncertainty propagation method is developed to propagate the uncertainties in PHCM constitutive parameters and microstructural variables to the model response variables of interest, such as stress, strain or macroscopic fatigue or damage measures, while avoiding expensive Monte Carlo simulations.

Thesis Defense: Zhaohao Fu, “Advances in Systems Science using Network Theory and Machine Learning”

THE DEPARTMENT OF CIVIL ENGINEERING

AND

ADVISOR TAKERU IGUSA, PROFESSOR

ANNOUNCE THE THESIS DEFENSE OF

Doctoral Candidate

Zhaohao Fu

Monday, January 14, 2019

3:00pm

Latrobe 106

“Advances in Systems Science using Network Theory and Machine Learning

Abstract:

Systems science is widely used for population, public health, traffic, hazard, and other scientific research. New challenges have come up regarding access to big data as well a deeper consideration of systems complexity. The overarching objective of the research herein is to apply modeling and analytic tools to study complex systems, with an emphasis on network theory and machine learning. Specifically, we analyze systems via methods such as agent-based modeling, dimension reduction, classification, and Monte Carlo sampling.

We begin by evaluating the effects of social networks in a migration setting. The application is on an NIH-funded study of the rural-to-urban mass migration in China since the 1980s, the largest human migration in history. We use a hierarchical social network by combing four layers of social networks. We study how endogenous social networks help explain accelerating trends in migration patterns. Then we develop this methodology further in an NSF-funded study of the societal impacts of repeated natural hazards. Here, clustering methods and the Exponential Random Graph Model (ERGM) are applied to model social network structures that respond to hazardous events. We demonstrate how mortality and behavior change by considering the role of social capital in social networks. A mathematical technique for generating random social networks that satisfies a set of social network properties is introduced. In our next study, we explore novel applications of machine learning, including dimension reduction and classification techniques, in the NIH-funded work of Boston Birth Cohort, a longitudinal study of 8,509 mother-child dyads, to analyze high-dimensional metabolomics data. We compare the performance of several machine learning methods and provide guidance for public health researchers to systematically approach other similar problems. In the final application in this thesis, we develop classification models to identify critical surge-producing storms in the Mid-Atlantic region. Here we demonstrate how expert opinion and analytical forms of domain knowledge can be incorporated with machine learning methods to build accurate storm surge prediction model.

Join Us for the Spring 2019 Seminar Series

Below is a listing for the speakers being showcased during the Spring 2019 Civil Engineering Graduate Seminar Series. All civil engineering graduate seminars are FREE and open to the public. Attendance is required for all enrolled Civil Engineering graduate students. For information on individual seminars, please refer to the Events Calendar.

For directions and information on parking please see Maps & Directions link at www.jhu.edu and select information on Homewood Campus.

Thesis Defense: Gary Lin, “Integrated Modeling of Complex Systems with Applications in Public Health and Sustainability”

THE DEPARTMENT OF CIVIL ENGINEERING

AND

ADVISOR TAKERU IGUSA, PROFESSOR

ANNOUNCE THE THESIS DEFENSE OF

Doctoral Candidate

Gary Lin

Monday, October 22, 2018

3:00pm

Malone 137

“Integrated Modeling of Complex Systems with Applications in Public Health and Sustainability

Abstract:

Understanding the dynamics of a changing world are of great interest to policy-makers, nonprofit organizations, governments, and businesses since society largely operates as a system. We develop system models to capture the complexity of the world in a logical and quantitative manner. Specifically, we use methods such as network analysis, time series analysis, system dynamics, and Markov Chains to explore systemic issues. These methods are applied to a socio-technical system related to public health and sustainability. We will also explore ways to capture this complexity by first identifying and analyzing the system with an interdisciplinary perspective then propose a method to integrate system models.

We begin by identifying the complexity of large-scale systems, such as Research & Development (R&D) of pharmaceutical treatments. In this project, we utilize a network representation to investigate collaboration among pharmaceutical companies and other stakeholders and determine the causes that enable success in developing a regulatory-approved therapeutic treatment. Afterward, we present an intermediate model that couples higher-scale and lower-scale models in an integrated heatwave resilience model. Thirdly, we propose an integrated multi-component model to capture the feedback loops that couples global population growth, environmental sustainability, and health systems. Finally, we investigate a system dynamics integration of a Markov Chain that describes migration patterns of the United States with respect to climate change.

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