Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- University of Oslo
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- University of Stavanger
- FCiências.ID
- UCL;
- UiT The Arctic University of Norway
- University of Bergen
- Aston University
- Cornell University
- Institute of Systems and Robotics-Faculty of Sciences and Technology of the University of Coimbra
- Instituto Politécnico de Coimbra
- NTNU - Norwegian University of Science and Technology
- NTNU Norwegian University of Science and Technology
- Nanyang Technological University
- National University of Singapore
- Politécnico de Leiria
- UNIVERSITY OF SURREY
- Universidade de Aveiro
- University of Beira Interior
- WESTERN SYDNEY UNIVERSITY
- 10 more »
- « less
-
Field
-
applications in chemical and pharmaceutical manufacturing; data-driven modelling and machine learning applications in process industries; advanced process control (APC); model predictive control (MPC); digital
-
characterisation and heat-transfer measurements, and development of model-predictive control algorithms for dynamic charge–discharge operation. The postholder will support prototype fabrication, instrumentation
-
with clinicians, neuroscientists, and molecular biologists to interpret AI-based findings and translate models into clinically actionable tools for early diagnosis, risk prediction, and patient
-
modern control theory (PID, adaptive, robust, and fractional control); Experience with fuzzy logic and AI-based control (reinforcement learning, neuro-fuzzy systems); Skills in modelling and simulation
-
contexts. This work will directly support the development of AI models to predict off-target effects across clinically relevant cell types, including primary cells and 3D organoid systems. Responsibilities
-
control and reinforcement learning Power system operations, planning, and electricity market design Transportation systems modeling and optimization Responsibilities: Postdoctoral fellows will: Develop
-
. Establish predictive modelling frameworks that describe the chemical evolution of carbon, hydrogen, and nitrogen species during ammonia-based fuel combustion. Analyze and quantify pollutant formation pathways
-
. The anticipation of more extended systems, although they probably will not resemble the solar system either, is challenging our standard models of planet formation. Our goal is to predict and reproduce
-
applications in chemical and pharmaceutical manufacturing; data-driven modelling and machine learning applications in process industries; advanced process control (APC); model predictive control (MPC); digital
-
biomolecules in a cellular society. The reasons for this behaviour remain poorly understood, and outside of some model species, this behaviour itself is poorly characterised. We have recently developed cross