Posting Duration: This posting will be open for application submissions for a minimum of seven (7) calendar days including the posting date.Sandia reserves the right to extend the posting date at any time.

The successful applicant will be working with other research programmers in the Karsan lab to develop data analysis pipelines for various next-generation sequencing (NGS) data such as—but not limited to—scRNA-Seq, targeted sequencing, ATAC-Seq, and BS-Seq

Posting Duration: This posting will be open for application submissions for a minimum of seven (7) calendar days including the posting date.Sandia reserves the right to extend the posting date at any time.

Posting Duration: This posting will be open for application submissions for a minimum of seven (7) calendar days including the posting date.Sandia reserves the right to extend the posting date at any time.

Posting Duration: This posting will be open for application submissions for a minimum of seven (7) calendar days including the posting date.Sandia reserves the right to extend the posting date at any time.

Posting Duration: This posting will be open for application submissions for a minimum of seven (7) calendar days including the posting date.Sandia reserves the right to extend the posting date at any time.

Data Scientist - Operations Research Data Scientist Operations Research for Chesterfield MO to contribute to development of analytics-driven product pipelines computational tools & databases for mining & visualizing large data sets; perform statistical analysis predictive modeling & experimental design; use advanced mathematical models machine learning algorithms & optimization methods to deliver business insights; develop reports on model inputs & outputs observed outputs & business performance; communicate analytical insights to cross-functional stakeholders to drive business decision-making.Requires Masters in Data Science Operations Research Industrial Engineering or closely related field & 2 yrs experience using CPLEX Xpress and/or Gurobi to build deterministic optimization algorithms; building data models using R and/or Python; developing predictive models using machine learning algorithms including Random Forest Ensembles & SVM with Scikit-learn Pandas and Numpy; designing experiments & identifying limitations in data; applying mathematical models simulations decision analysis stochastic models system dynamics & forecasting; and communicating quantitative analyses to technical & non-technical stakeholders.

Within CASA, the Computational Science group focuses on numerical methods for (partial) differential equations, model order reduction, and scientific machine learning, particularly for the efficient solution of differential equations in the context of data assimilation, control, optimization, inverse problems, Bayesian methods, uncertainty quantification, and optimal experimental design.

You will be working within our Simulations & High Performance Computing Team, a team of engineers and scientists dedicated to develop massively parallel toolkits to deign superconducting quantum processors. You will focus on developing software tools for the design and simulation of quantum devices which span multiple scales, involve complex geometries, and incorporate a number of physical phenomena. You will collaborate closely with other teams when required. In this role, you will draw on your strong expertise as well as as your critical thinking and obsession for documentation.

You will join our passionate Research Informatics team to deliver solutions by using Agile and DevOps methodologies. You will also support our Research team in crafting and implementing IS solutions for Discovery, Non-Clinical & Early Development. Our success in informatics is inspired by our dedication to our patients, to our business partners, and handle to use technologies that deliver critical business value.

The candidate should also have experience with multiple modern techniques and algorithms in machine learning and statistical computing such as regression, clustering/segmentation, time-series analysis, recommender systems, predictive analytics, Bayesian modeling, data fusion, supervised/unsupervised learning, decision trees, A/B testing, natural language processing.

The Department of the Built Environment is looking for a passionate Computational Design Specialist to support education and teaching activities within the department, collaborating with the Architectural Design and Engineering Chair, the Unit of Structural Engineering and Design and Information Systems in the Built Environment.

We have an excellent opportunity for an applied mathematician working at the forefront of Scientific Computing to join us as Tenure-Track Assistant Professor. This vacancy is part of the Irène Curie Fellowship and is currently open only for female candidates.

Canada’s Michael Smith Genome Sciences Centre (GSC) is a state-of-the-art, large scale, high throughput, clinically accredited genomics and bioinformatics facility. We are currently looking for a Postdoctoral Fellow to join Dr. Adi Steif’s computational biology group.

Lawrence Berkeley National Lab’s (LBNL) Applied Mathematics and Computational Research Division has an opening for a Postdoctoral Scholar (Computational Research) in applying machine learning tools to optimization, sampling and uncertainty quantification. The Center for Computational Sciences and Engineering (CCSE) develops and applies advanced computational methodologies to solve large-scale scientific and engineering problems arising in DOE mission areas involving energy, environment, science and industrial technology.

We are looking for highly motivated individuals with a strong technical background in theoretical condensed matter physics and materials science combined with experience operating in a high performance computational environment afforded, for example, by DOE supercomputing facilities at Oak Ridge National Lab and Argonne National Lab.

We are seeking an experienced Computational Scientist who will apply computational methods to obtain structural models from cryo-EM data including docking and molecular model building guided by structural biology data, to support Gandeeva Drug Discovery Programs.

Work along withScientificComputing worldwide team to develop statistical analysis process, plans and determine appropriate statistical methodology for data analysis as part of the study protocol and design.

The Multiscale Methods (MM) Group at Oak Ridge National Laboratory (ORNL) is seekly research scientists with specialties in a variety of computational mathematics areas, including multiscale modeling and simulations, physics-based and data-driven model reduction, iterative solvers, scalable algorithms (including mathematical software), uncertainty quantification, and optimization.

You are a dynamic data scientist interested in challenging the status quo to ensure development and impact of Sanofi's AI solutions for the patients of tomorrow. You are an influencer and leader who has deployed AI/ML solutions applying state-of-the art algorithms with technically robust lifecycle management. You have a keen eye for improvement opportunities and a demonstrated ability to deliver AI/ML solutions while working across different technologies and in a cross-functional environment.