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Details Of Quantitative Risk Modeling Interdisciplinary Instructional Training Registration (KNUST)

Details Of Quantitative Risk Modeling Interdisciplinary Instructional Training Registration (KNUST)

Sponsored by

Safe Water for Food (SaWaFo) Project

Participant of Quantitative Risk Modeling Interdisciplinary Instructional Training will learn the underlying concepts how to use scientific data to perform risk modeling and other quantitative modeling techniques. The Instructional Training is design for graduate students, doctoral and post-doctoral fellows and early –career professionals/Scientist. Participants will gain hands-on experience with host of quantitative risk modeling software options and other tools while they participate in realistic case studies.

Participants will:

  • Participate in discussion forum and instructional lectures
  • Engage in specific hands on exercises

We assume no prior knowledge in quantitative modeling experience on the part of participants; therefore we will include tutorial models and applications for quantitative risk modeling.

No registration fee is required. However, only limited seats are available.

Accepting applications until January 31st, 2017, acceptance notification will be released by February 2nd, 2017

Registration Information

  • Name
  • Institution
  • Discipline
  • Motivation (Max: 150 words)
  • Rank e.g PG Student, PG Doctoral student, early career professional/scientist

Should be sent to the following email:

Edjowusu-ansah.cos@knust.edu.gh or degraftt@gmail.com

Program Contact

Dr. Emmanuel de-Graft Johnson Owusu-Ansah or Rejoice Ametepeh

0244378150

Training Includes:

  • Microbial Hazards Identification
  • Chemical Hazards Identification
  • Quantification and Fitting of probabilistic distributions on Data
  • Quantitative Modeling Techniques
  • Understanding and Selection of Probabilistic Distributions
  • Expert Judgment Elicitation
  • Modeling with Hardly any Data
  • Chemical Risk Assessment and Hazard Quotient
  • Microbial Risk Assessment
  • Uncertainty Quantification in Risk Models and Sensitivity Analysis.

NB: Participants with physical science and engineering backgrounds can also apply because the quantitative modeling approach can be applied in all other fields of science.

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