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As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skill sets that are relevant to industry demands while working on research projects in SIT.

The researcher will be part of the team of the MCCS NATURE Project (https://www.nparks.gov.sg/Cuge/Programmes-Schemes/Research%20Programmes/MCCS%20Programme). The primary role involves developing and validating CFD models to study wave attenuation by mangroves, seagrass, and coral. Responsibilities include calibrating simulations with experimental data, analyzing shear coefficients and drag forces, and contributing to interdisciplinary research on coastal protection, with an emphasis on delivering accurate and impactful modeling insights.

Key Responsibilities

• Participate in and manage the research project with Principal Investigator (PI), Co-PI and the research team members to ensure all project deliverables are met.
• Undertake these responsibilities in the project:

1. Wave Stochastic Analysis

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RSS Feed Source: Academic Keys

As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skill sets that are relevant to industry demands while working on research projects in SIT.

The researcher will be part of the team of the MCCS NATURE Project (https://www.nparks.gov.sg/Cuge/Programmes-Schemes/Research%20Programmes/MCCS%20Programme). The primary role involves developing and validating CFD models to study wave attenuation by mangroves, seagrass, and coral. Responsibilities include calibrating simulations with experimental data, analyzing shear coefficients and drag forces, and contributing to interdisciplinary research on coastal protection, with an emphasis on delivering accurate and impactful modeling insights.

Key Responsibilities

Participate in and manage the research project with Principal Investigator (PI), Co-PI and the research team members to ensure all project deliverables are met.

Undertake these responsibilities in the project:

1. Computational Fluid Dynamics

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RSS Feed Source: Academic Keys

As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skill sets that are relevant to industry demands while working on research projects in SIT. 

The researcher will be part of the team of the MCCS Project (https://www.nparks.gov.sg/Cuge/Programmes-Schemes/Research%20Programmes/MCCS%201st%20Grant%20Call). The Research Engineer will focus on hydrodynamic analysis, finite element modeling, and stability assessment for modular floating structures. Responsibilities include designing and evaluating system performance through simulations, conducting site test-bedding to validate designs, and collecting and analyzing in-situ data. The role also involves preparing technical reports, collaborating with multidisciplinary teams, and staying updated with advancements in the field. This position requires expertise in hydrodynamics, FEM, stability modeling, and a hands-on approach to field testing and reporting.

Key Responsibilities

Undertake these responsibilities in the project:

1. Hydrodynamic Analysis:
• Perform

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RSS Feed Source: Academic Keys

Samuel Kaski’s research group Probabilistic Machine Learning is searching for postdocs to work on AI fundamentals in exciting projects. The work includes collaboration with the Finnish Center for Artificial Intelligence (FCAI), the Centre for AI Fundamentals at the University of Manchester, the Alan Turing Institute, ELLIS and the new ELLIS Institute Finland, and researchers from other fields.

Prof Kaski is Professor of Computer Science in Aalto University and Professor of AI in the University of Manchester. He is Director of Finnish Center for Artificial Intelligence and ELLIS Unit Helsinki. His research group develops machine learning principles and methods focusing on a few key topics (see “Machine learning foundations” below), often working with researchers of other fields in new exciting applications (see the other topics below).

Machine learning foundations
Keywords: probabilistic modelling, Bayesian inference, simulation-based / likelihood-free inference, multi-agent RL and collaborative AI, sequential decision

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